Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
126 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancer (2405.04078v1)

Published 7 May 2024 in cs.LG, cs.AI, and q-bio.QM

Abstract: Cancer, a leading cause of death globally, occurs due to genomic changes and manifests heterogeneously across patients. To advance research on personalized treatment strategies, the effectiveness of various drugs on cells derived from cancers (`cell lines') is experimentally determined in laboratory settings. Nevertheless, variations in the distribution of genomic data and drug responses between cell lines and humans arise due to biological and environmental differences. Moreover, while genomic profiles of many cancer patients are readily available, the scarcity of corresponding drug response data limits the ability to train machine learning models that can predict drug response in patients effectively. Recent cancer drug response prediction methods have largely followed the paradigm of unsupervised domain-invariant representation learning followed by a downstream drug response classification step. Introducing supervision in both stages is challenging due to heterogeneous patient response to drugs and limited drug response data. This paper addresses these challenges through a novel representation learning method in the first phase and weak supervision in the second. Experimental results on real patient data demonstrate the efficacy of our method (WISER) over state-of-the-art alternatives on predicting personalized drug response.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (51)
  1. Predicting breast cancer from risk factors using svm and extra-trees-based feature selection method. Computers, 11(9):136, 2022.
  2. Improving cell type identification with gaussian noise-augmented single-cell rna-seq contrastive learning. Briefings in Functional Genomics, pp.  elad059, 2024.
  3. Towards robust interpretability with self-explaining neural networks. Advances in neural information processing systems, 31, 2018.
  4. Wasserstein generative adversarial networks. In International conference on machine learning, pp.  214–223. PMLR, 2017.
  5. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014.
  6. Unbiased supervised contrastive learning. arXiv preprint arXiv:2211.05568, 2022.
  7. Tumour heterogeneity in the clinic. Nature, 501(7467):355–364, 2013.
  8. Domain separation networks. Advances in neural information processing systems, 29, 2016.
  9. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell, 167(1):260–274, 2016.
  10. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pp.  1597–1607. PMLR, 2020.
  11. Maximum likelihood estimation of observer error-rates using the em algorithm. Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(1):20–28, 1979.
  12. Adversarial deconfounding autoencoder for learning robust gene expression embeddings. Bioinformatics, 36(Supplement_2):i573–i582, 2020.
  13. Fast and three-rious: Speeding up weak supervision with triplet methods. In ICML, pp.  3280–3291. PMLR, 2020.
  14. Domain-adversarial training of neural networks. The journal of machine learning research, 17(1):2096–2030, 2016.
  15. Next-generation characterization of the cancer cell line encyclopedia. Nature, 569(7757):503–508, 2019.
  16. Dissecting supervised contrastive learning. In International Conference on Machine Learning, pp.  3821–3830. PMLR, 2021.
  17. Neural turing machines. arXiv preprint arXiv:1410.5401, 2014.
  18. Improved training of wasserstein gans. Advances in neural information processing systems, 30, 2017.
  19. A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening. Nature Machine Intelligence, 4(10):879–892, 2022.
  20. In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737, 2017.
  21. Autoencoders, minimum description length and helmholtz free energy. Advances in neural information processing systems, 6, 1993.
  22. The cancer genome atlas: creating lasting value beyond its data. Cell, 173(2):283–285, 2018.
  23. Deep generative neural network for accurate drug response imputation. Nature communications, 12(1):1740, 2021.
  24. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of naacL-HLT, volume 1, pp.  2, 2019.
  25. Supervised contrastive learning. Advances in neural information processing systems, 33:18661–18673, 2020.
  26. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
  27. Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer cell, 38(5):672–684, 2020.
  28. Training subset selection for weak supervision. Advances in Neural Information Processing Systems, 35:16023–16036, 2022.
  29. Attention-guided supervised contrastive learning for semantic segmentation. arXiv preprint arXiv:2106.01596, 2021.
  30. Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients. Nature Cancer, 2(2):233–244, 2021.
  31. Adversarial multi class learning under weak supervision with performance guarantees. In International Conference on Machine Learning, pp.  7534–7543. PMLR, 2021.
  32. Identifying and handling mislabelled instances. Journal of Intelligent Information Systems, 22(1):89–109, 2004.
  33. A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10):1345–1359, 2009.
  34. Snorkel: Rapid training data creation with weak supervision. In Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases, volume 11, pp.  269. NIH Public Access, 2017.
  35. Data programming: Creating large training sets, quickly. NeurIPS, 29, 2016.
  36. A deep learning framework for predicting response to therapy in cancer. Cell reports, 29(11):3367–3373, 2019.
  37. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.  815–823, 2015.
  38. Seyhan, A. A. Lost in translation: the valley of death across preclinical and clinical divide–identification of problems and overcoming obstacles. Translational Medicine Communications, 4(1):1–19, 2019.
  39. Aitl: adversarial inductive transfer learning with input and output space adaptation for pharmacogenomics. Bioinformatics, 36(Supplement_1):i380–i388, 2020.
  40. Out-of-distribution generalization from labelled and unlabelled gene expression data for drug response prediction. Nature Machine Intelligence, 3(11):962–972, 2021.
  41. Fusing conditional submodular gan and programmatic weak supervision. arXiv preprint arXiv:2312.10366, 2023.
  42. Gdisc: a web portal for integrative analysis of gene–drug interaction for survival in cancer. Bioinformatics, 33(9):1426–1428, 2017.
  43. Deep coral: Correlation alignment for deep domain adaptation. In Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III 14, pp.  443–450. Springer, 2016.
  44. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017.
  45. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning, pp.  1096–1103, 2008.
  46. The coming decade in precision oncology: six riddles. Nature Reviews Cancer, 23(1):43–54, 2023.
  47. Global computational alignment of tumor and cell line transcriptional profiles. Nature Communications, 12(1):22, 2021.
  48. WHO. Cancer. https://www.who.int/news-room/fact-sheets/detail/cancer, February 2022.
  49. Barlow twins: Self-supervised learning via redundancy reduction. In International conference on machine learning, pp.  12310–12320. PMLR, 2021.
  50. A survey on programmatic weak supervision. arXiv preprint arXiv:2202.05433, 2022.
  51. Enhanced co-expression extrapolation (coxen) gene selection method for building anti-cancer drug response prediction models. Genes, 11(9):1070, 2020.
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets