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MC-NN: An End-to-End Multi-Channel Neural Network Approach for Predicting Influenza A Virus Hosts and Antigenic Types (2306.05587v4)
Published 8 Jun 2023 in cs.LG and q-bio.QM
Abstract: Influenza poses a significant threat to public health, particularly among the elderly, young children, and people with underlying dis-eases. The manifestation of severe conditions, such as pneumonia, highlights the importance of preventing the spread of influenza. An accurate and cost-effective prediction of the host and antigenic sub-types of influenza A viruses is essential to addressing this issue, particularly in resource-constrained regions. In this study, we propose a multi-channel neural network model to predict the host and antigenic subtypes of influenza A viruses from hemagglutinin and neuraminidase protein sequences. Our model was trained on a comprehensive data set of complete protein sequences and evaluated on various test data sets of complete and incomplete sequences. The results demonstrate the potential and practicality of using multi-channel neural networks in predicting the host and antigenic subtypes of influenza A viruses from both full and partial protein sequences.
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Orthomyxoviridae, p 1151–1185. fields virology (2013). [8] Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Lau, L. L. et al. Viral shedding and clinical illness in naturally acquired influenza virus infections. The Journal of infectious diseases 201 (10), 1509–1516 (2010) . [3] Wilde, J. A. et al. Effectiveness of influenza vaccine in health care professionals: a randomized trial. Jama 281 (10), 908–913 (1999) . [4] Watanabe, T. Renal complications of seasonal and pandemic influenza a virus infections. European journal of pediatrics 172 (1), 15–22 (2013) . [5] Casas-Aparicio, G. A. et al. Aggressive fluid accumulation is associated with acute kidney injury and mortality in a cohort of patients with severe pneumonia caused by influenza a h1n1 virus. PLoS One 13 (2), e0192592 (2018) . [6] England, P. H. Influenza: the green book, chapter 19 (2020) . [7] Shaw, M. & Palese, P. Orthomyxoviridae, p 1151–1185. fields virology (2013). [8] Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Wilde, J. A. et al. Effectiveness of influenza vaccine in health care professionals: a randomized trial. Jama 281 (10), 908–913 (1999) . [4] Watanabe, T. Renal complications of seasonal and pandemic influenza a virus infections. European journal of pediatrics 172 (1), 15–22 (2013) . [5] Casas-Aparicio, G. A. et al. Aggressive fluid accumulation is associated with acute kidney injury and mortality in a cohort of patients with severe pneumonia caused by influenza a h1n1 virus. PLoS One 13 (2), e0192592 (2018) . [6] England, P. H. Influenza: the green book, chapter 19 (2020) . [7] Shaw, M. & Palese, P. Orthomyxoviridae, p 1151–1185. fields virology (2013). [8] Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Watanabe, T. Renal complications of seasonal and pandemic influenza a virus infections. European journal of pediatrics 172 (1), 15–22 (2013) . [5] Casas-Aparicio, G. A. et al. Aggressive fluid accumulation is associated with acute kidney injury and mortality in a cohort of patients with severe pneumonia caused by influenza a h1n1 virus. PLoS One 13 (2), e0192592 (2018) . [6] England, P. H. Influenza: the green book, chapter 19 (2020) . [7] Shaw, M. & Palese, P. Orthomyxoviridae, p 1151–1185. fields virology (2013). [8] Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. 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[15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. 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Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). England, P. H. Influenza: the green book, chapter 19 (2020) . [7] Shaw, M. & Palese, P. Orthomyxoviridae, p 1151–1185. fields virology (2013). [8] Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. 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[16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Shaw, M. & Palese, P. Orthomyxoviridae, p 1151–1185. fields virology (2013). [8] Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. 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[18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. 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Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. 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Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. 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Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. 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Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. 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Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . 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[7] Shaw, M. & Palese, P. Orthomyxoviridae, p 1151–1185. fields virology (2013). [8] Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Watanabe, T. Renal complications of seasonal and pandemic influenza a virus infections. European journal of pediatrics 172 (1), 15–22 (2013) . [5] Casas-Aparicio, G. A. et al. Aggressive fluid accumulation is associated with acute kidney injury and mortality in a cohort of patients with severe pneumonia caused by influenza a h1n1 virus. PLoS One 13 (2), e0192592 (2018) . [6] England, P. H. Influenza: the green book, chapter 19 (2020) . [7] Shaw, M. & Palese, P. Orthomyxoviridae, p 1151–1185. fields virology (2013). [8] Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Casas-Aparicio, G. A. et al. Aggressive fluid accumulation is associated with acute kidney injury and mortality in a cohort of patients with severe pneumonia caused by influenza a h1n1 virus. PLoS One 13 (2), e0192592 (2018) . [6] England, P. H. Influenza: the green book, chapter 19 (2020) . [7] Shaw, M. & Palese, P. Orthomyxoviridae, p 1151–1185. fields virology (2013). [8] Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). England, P. H. Influenza: the green book, chapter 19 (2020) . [7] Shaw, M. & Palese, P. Orthomyxoviridae, p 1151–1185. fields virology (2013). [8] Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. 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IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. 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Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). 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[36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. 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[15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. 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Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. 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Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. 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[29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . 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Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). 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Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . 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[29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . 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Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. 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Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). England, P. H. Influenza: the green book, chapter 19 (2020) . [7] Shaw, M. & Palese, P. Orthomyxoviridae, p 1151–1185. fields virology (2013). [8] Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. 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[16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Shaw, M. & Palese, P. Orthomyxoviridae, p 1151–1185. fields virology (2013). [8] Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. 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[18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. 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[15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. 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Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. 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Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. 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Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. 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Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). 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Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). England, P. H. Influenza: the green book, chapter 19 (2020) . [7] Shaw, M. & Palese, P. Orthomyxoviridae, p 1151–1185. fields virology (2013). [8] Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Shaw, M. & Palese, P. Orthomyxoviridae, p 1151–1185. fields virology (2013). [8] Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. 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The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . 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Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. 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IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. 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Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. 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Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. 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Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . 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[30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. 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O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Shaw, M. & Palese, P. Orthomyxoviridae, p 1151–1185. fields virology (2013). [8] Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. 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[25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. 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Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). 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Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). 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[36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . 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Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. 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Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . 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Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). 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Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. 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[29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . 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Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). 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Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. 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IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. 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Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. 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[47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. 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View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Goadrich, M., Oliphant, L. & Shavlik, J. 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Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Mills, C. E., Robins, J. M. & Lipsitch, M. Transmissibility of 1918 pandemic influenza. Nature 432 (7019), 904–906 (2004) . [9] Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Asha, K. & Kumar, B. Emerging influenza d virus threat: what we know so far! Journal of Clinical Medicine 8 (2), 192 (2019) . [10] James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). James, S. H. & Whitley, R. J. in Influenza viruses 1465–1471 (Elsevier, 2017). [11] Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . 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Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. 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Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. 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[15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. 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Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. 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Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. 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[29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . 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Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). 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Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . 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[29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . 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Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. 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[16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. 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[29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. 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[40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. 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Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. 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[48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . 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Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. 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[40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. 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Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. 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Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. 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[30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . 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Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. 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Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. 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[39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. 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Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Clayville, L. R. Influenza update: a review of currently available vaccines. Pharmacy and Therapeutics 36 (10), 659 (2011) . [12] Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. 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Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. 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Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. 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Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. 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Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. 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Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. 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Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . 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Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Vemula, S. V. et al. Current approaches for diagnosis of influenza virus infections in humans. Viruses 8 (4), 96 (2016) . [13] Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . 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Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). 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Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. 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[29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . 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Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. 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A relationship between the average precision and the area under the roc curve, 349–352 (2015). Fabijańska, A. & Grabowski, S. Viral genome deep classifier. IEEE Access 7, 81297–81307 (2019) . [14] Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. 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Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Scarafoni, D., Telfer, B. A., Ricke, D. O., Thornton, J. R. & Comolli, J. Predicting influenza a tropism with end-to-end learning of deep networks. Health security 17 (6), 468–476 (2019) . [15] Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. 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[48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. 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IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. 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IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. 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Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. 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Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. 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Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Goadrich, M., Oliphant, L. & Shavlik, J. 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[20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Ahsan, R. & Ebrahimi, M. The first implication of image processing techniques on influenza a virus sub-typing based on ha/na protein sequences, using convolutional deep neural network. bioRxiv 448159 (2018) . [16] Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Xu, B., Tan, Z., Li, K., Jiang, T. & Peng, Y. Predicting the host of influenza viruses based on the word vector. PeerJ 5, e3579 (2017) . [17] Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kincaid, C. N-gram methods for influenza host classification, 105–107 (The Steering Committee of The World Congress in Computer Science, Computer …, 2018). [18] Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . 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[39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. 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[40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. 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[39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. 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Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). 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[40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. 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A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. 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Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Attaluri, P. K., Chen, Z., Weerakoon, A. M. & Lu, G. Integrating decision tree and hidden markov model (hmm) for subtype prediction of human influenza a virus, 52–58 (Springer, 2009). [19] Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . 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[39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. 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Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . 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IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. 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[39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. 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[46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . 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Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. 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Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. 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[48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . 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Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. 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View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Goadrich, M., Oliphant, L. & Shavlik, J. 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Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). 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[36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Eng, C. L., Tong, J. C. & Tan, T. W. Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. 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Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . 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Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015).
- Predicting host tropism of influenza a virus proteins using random forest. BMC medical genomics 7 (3), 1–11 (2014) . [20] Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Kwon, E., Cho, M., Kim, H. & Son, H. S. A study on host tropism determinants of influenza virus using machine learning. Current Bioinformatics 15 (2), 121–134 (2020) . [21] Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Mock, F., Viehweger, A., Barth, E. & Marz, M. Vidhop, viral host prediction with deep learning. Bioinformatics 37 (3), 318–325 (2021) . [22] Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Attaluri, P. K., Chen, Z. & Lu, G. Applying neural networks to classify influenza virus antigenic types and hosts, 1–6 (IEEE, 2010). [23] Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. Hopper: an adaptive model for probability estimation of influenza reassortment through host prediction. BMC medical genomics 13 (1), 1–13 (2020) . [26] George, A. et al. Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security 15, 42–55 (2019) . [27] Chen, Y. et al. A multi-channel deep neural network for relation extraction. IEEE Access 8, 13195–13203 (2020) . [28] Cao, Y., Liu, Z., Li, C., Li, J. & Chua, T.-S. Multi-channel graph neural network for entity alignment. arXiv preprint arXiv:1908.09898 (2019) . [29] Yang, Y., Wu, Q., Qiu, M., Wang, Y. & Chen, X. Emotion recognition from multi-channel eeg through parallel convolutional recurrent neural network, 1–7 (IEEE, 2018). [30] Kerzel, M., Ali, M., Ng, H. G. & Wermter, S. Haptic material classification with a multi-channel neural network, 439–446 (IEEE, 2017). [31] Squires, R. B. et al. Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and other respiratory viruses 6 (6), 404–416 (2012) . [32] Shu, Y. & McCauley, J. Gisaid: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance 22 (13), 30494 (2017) . [33] Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10 (11), e0141287 (2015) . [34] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 220, 104740 (2022) . [35] Xu, Y. & Wojtczak, D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. arXiv preprint arXiv:2207.13842 (2022) . [36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Chrysostomou, C., Alexandrou, F., Nicolaou, M. A. & Seker, H. Classification of influenza hemagglutinin protein sequences using convolutional neural networks. arXiv preprint arXiv:2108.04240 (2021) . [24] Sherif, F. F., Zayed, N. & Fakhr, M. Classification of host origin in influenza a virus by transferring protein sequences into numerical feature vectors. Int J Biol Biomed Eng 11 (2017) . [25] Yin, R., Zhou, X., Rashid, S. & Kwoh, C. K. 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Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. 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Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. 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[45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. 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Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. 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Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. 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Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. 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Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. 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Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. 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[36] Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. 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Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . 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[48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. 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Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Goadrich, M., Oliphant, L. & Shavlik, J. 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[48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches (2014). 1409.1259. [37] Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017) . [38] Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Grechishnikova, D. Transformer neural network for protein-specific de novo drug generation as a machine translation problem. Scientific reports 11 (1), 1–13 (2021) . [39] Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) . [40] Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) . [41] Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. A relationship between the average precision and the area under the roc curve, 349–352 (2015). Brown, T. et al. Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) . [42] Akosa, J. Predictive accuracy: A misleading performance measure for highly imbalanced data, Vol. 12 (2017). [43] Davis, J. & Goadrich, M. The relationship between precision-recall and roc curves, 233–240 (2006). [44] Bunescu, R. et al. Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine 33 (2), 139–155 (2005) . [45] Bockhorst, J. & Craven, M. Markov networks for detecting overlapping elements in sequence data. Advances in Neural Information Processing Systems 17, 193–200 (2005) . [46] Goadrich, M., Oliphant, L. & Shavlik, J. Learning ensembles of first-order clauses for recall-precision curves: A case study in biomedical information extraction, 98–115 (Springer, 2004). [47] Davis, J. et al. View learning for statistical relational learning: With an application to mammography., 677–683 (Citeseer, 2005). [48] Su, W., Yuan, Y. & Zhu, M. 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