Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data (2402.12391v2)

Published 15 Feb 2024 in q-bio.GN, cs.AI, and cs.LG

Abstract: Machine learning has emerged as a powerful tool for scientific discovery, enabling researchers to extract meaningful insights from complex datasets. For instance, it has facilitated the identification of disease-predictive genes from gene expression data, significantly advancing healthcare. However, the traditional process for analyzing such datasets demands substantial human effort and expertise for the data selection, processing, and analysis. To address this challenge, we introduce a novel framework, a Team of AI-made Scientists (TAIS), designed to streamline the scientific discovery pipeline. TAIS comprises simulated roles, including a project manager, data engineer, and domain expert, each represented by a LLM. These roles collaborate to replicate the tasks typically performed by data scientists, with a specific focus on identifying disease-predictive genes. Furthermore, we have curated a benchmark dataset to assess TAIS's effectiveness in gene identification, demonstrating our system's potential to significantly enhance the efficiency and scope of scientific exploration. Our findings represent a solid step towards automating scientific discovery through LLMs.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (60)
  1. H. Abusamra. A comparative study of feature selection and classification methods for gene expression data of glioma. Procedia Computer Science, 23:5–14, 2013.
  2. A. A. Awomoyi. The human solute carrier family 11 member 1 protein (slc11a1): linking infections, autoimmunity and cancer? FEMS Immunology & Medical Microbiology, 49(3):324–329, 2007.
  3. Vitamin d: modulator of the immune system. Current opinion in pharmacology, 10(4):482–496, 2010.
  4. Chemcrow: Augmenting large-language models with chemistry tools. arXiv preprint arXiv: 2304.05376, 2023.
  5. Mammaprint™: a comprehensive review. Future oncology, 15(2):207–224, 2019.
  6. Gene: a gene-centered information resource at ncbi. Nucleic acids research, 43(D1):D36–D42, 2015.
  7. Confounding factors in the transcriptome analysis of an in-vivo exposure experiment. PLoS One, 11(1):e0145252, 2016.
  8. Personalized medicine: progress and promise. Annual review of genomics and human genetics, 12:217–244, 2011.
  9. E. Clough and T. Barrett. The gene expression omnibus database. Methods in Molecular Biology, 1418:93–110, 2016. doi: 10.1007/978-1-4939-3578-9˙5.
  10. Modulation of inflammatory and immune responses by vitamin d. Journal of autoimmunity, 85:78–97, 2017.
  11. Self-collaboration code generation via chatgpt. arXiv preprint arXiv: 2304.07590, 2023.
  12. Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv: 2305.14325, 2023.
  13. Towards revealing the mystery behind chain of thought: A theoretical perspective. NEURIPS, 2023.
  14. D. Ghosh and A. M. Chinnaiyan. Classification and selection of biomarkers in genomic data using lasso. Journal of Biomedicine and Biotechnology, 2005(2):147, 2005.
  15. Visualizing and interpreting cancer genomics data via the xena platform. Nature biotechnology, 38(6):675–678, 2020.
  16. What can large language models do in chemistry? a comprehensive benchmark on eight tasks. arXiv preprint arXiv:2305.18365, 2023.
  17. The path to personalized medicine. New England Journal of Medicine, 363(4):301–304, 2010.
  18. Reasoning with language model is planning with world model. Conference on Empirical Methods in Natural Language Processing, 2023. doi: 10.48550/arXiv.2305.14992.
  19. Metagpt: Meta programming for a multi-agent collaborative framework. arXiv preprint arXiv: 2308.00352, 2023.
  20. From big data to better patient outcomes. Clinical Chemistry and Laboratory Medicine (CCLM), 61(4):580–586, 2023. doi: 10.1515/cclm-2022-1096. URL https://doi.org/10.1515/cclm-2022-1096.
  21. I. M. Johnstone. On the distribution of the largest eigenvalue in principal components analysis. The Annals of statistics, 29(2):295–327, 2001.
  22. M. M. R. Khondoker. Statistical methods for pre-processing microarray gene expression data. PhD thesis, University of Edinburgh, 2006.
  23. Race, gene expression signatures, and clinical outcomes of patients with high-risk early breast cancer. JAMA Network Open, 6(12):e2349646–e2349646, 2023.
  24. Tackling the widespread and critical impact of batch effects in high-throughput data. Nature Reviews Genetics, 11(10):733–739, 2010.
  25. Theory of mind for multi-agent collaboration via large language models. arXiv preprint arXiv:2310.10701, 2023.
  26. Fast linear mixed models for genome-wide association studies. Nature methods, 8(10):833–835, 2011.
  27. Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation, 2023.
  28. Large language models generate functional protein sequences across diverse families. Nature Biotechnology, pages 1–8, 2023.
  29. Individualization of therapy using mammaprint® ì: from development to the mindact trial. Cancer genomics & proteomics, 4(3):147–155, 2007.
  30. Skeleton-of-thought: Large language models can do parallel decoding. arXiv preprint arXiv:2307.15337, 2023.
  31. OpenAI. Gpt-4 technical report. PREPRINT, 2023.
  32. Communicative agents for software development. arXiv preprint arXiv: 2307.07924, 2023.
  33. Novel precision medicine approaches and treatment strategies in hematological malignancies. Journal of Internal Medicine, 294(4):413–436, 2023.
  34. Conceptual framework for autonomous cognitive entities. arXiv preprint arXiv: 2310.06775, 2023.
  35. Large language models encode clinical knowledge. Nature, 620(7972):172–180, 2023.
  36. Cognitive architectures for language agents. arXiv preprint arXiv: 2309.02427, 2023.
  37. Y. Talebirad and A. Nadiri. Multi-agent collaboration: Harnessing the power of intelligent llm agents. arXiv preprint arXiv: 2306.03314, 2023.
  38. R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1):267–288, 1996.
  39. The cancer genome atlas (tcga): an immeasurable source of knowledge. Contemporary Oncology (Poznan), 19(1A):A68–77, 2015. doi: 10.5114/wo.2014.47136.
  40. Llama: Open and efficient foundation language models. arXiv preprint arXiv: 2302.13971, 2023a.
  41. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv: 2307.09288, 2023b.
  42. Gene expression profiling predicts clinical outcome of breast cancer. nature, 415(6871):530–536, 2002.
  43. Towards understanding chain-of-thought prompting: An empirical study of what matters. arXiv preprint arXiv:2212.10001, 2022a.
  44. Variable selection in heterogeneous datasets: A truncated-rank sparse linear mixed model with applications to genome-wide association studies. In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 431–438. IEEE, 2017.
  45. Trade-offs of linear mixed models in genome-wide association studies. Journal of Computational Biology, 29(3):233–242, 2022b.
  46. Adapting llm agents through communication. arXiv preprint arXiv: 2310.01444, 2023a.
  47. A survey on large language model based autonomous agents. arXiv preprint arXiv:2308.11432, 2023b.
  48. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171, 2022c.
  49. Unleashing the emergent cognitive synergy in large language models: A task-solving agent through multi-persona self-collaboration. arXiv preprint arXiv:2307.05300, 2023c.
  50. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824–24837, 2022.
  51. Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics, 25(6):714–721, 2009.
  52. Auto-gpt for online decision making: Benchmarks and additional opinions. arXiv preprint arXiv: 2306.02224, 2023a.
  53. An overview of the use of precision population medicine in cancer care: First of a series. Cureus, 15(4), 2023b.
  54. Large language models in health care: Development, applications, and challenges. Health Care Science, 2(4):255–263, 2023c.
  55. React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629, 2022.
  56. Socs1 and its potential clinical role in tumor. Pathology & Oncology Research, 25(4):1295–1301, 2019.
  57. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature genetics, 38(2):203–208, 2006.
  58. Synapse: Leveraging few-shot exemplars for human-level computer control. arXiv preprint arXiv:2306.07863, 2023.
  59. How far are large language models from agents with theory-of-mind? arXiv preprint arXiv: 2310.03051, 2023a.
  60. Agents: An open-source framework for autonomous language agents. arXiv preprint arXiv:2309.07870, 2023b.
Citations (1)
View on Semantic Scholar

Summary

  • The paper introduces TAIS, a framework where specialized AI agents collaboratively automate genomic discovery by simulating roles like data engineering and statistical analysis.
  • It employs innovative one- and two-step regression analyses along with eigenvalue gap detection for confounding factors to accurately identify disease-predictive genes.
  • The framework achieved a 45.73% success rate on the GenQEX benchmark, demonstrating its potential to reduce manual effort in complex gene expression analyses.

An Academic Assessment of "Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data"

The paper "Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data" introduces an innovative framework named Team of AI-made Scientists (TAIS), which aims to automate the scientific discovery process in genomics utilizing LLMs. The primary objective is to streamline the identification of disease-predictive genes from gene expression datasets, such as those available from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO).

Overview of the TAIS Framework

TAIS is conceptualized as a multi-agent system consisting of simulated roles including Project Managers, Data Engineers, Domain Experts, Statisticians, and Code Reviewers, each represented by a separate LLM. These agents collectively perform tasks traditionally associated with data science workflows, specifically focusing on the preprocessing of input data, regression analyses, and the identification of confounding factors. The data input primarily involves gene expression data and clinical information extracted from established public databases.

Methodological Contributions

The methodological structure of TAIS is of particular interest, emphasizing role specialization and interaction amongst AI agents for task execution. The paper introduces a systematic program-and-review protocol whereby Code Reviewers assess and refine the output of Statisticians and Data Engineers, aiming to enhance the precision and effectiveness of the generated analysis code.

Moreover, the paper introduces a sophisticated regression analysis strategy to tackle the high-dimensional and heterogeneous nature of genomic data. The paper details both single-step and two-step regression analyses, with the latter employing a novel two-phase approach to estimate missing conditions in datasets. This is complemented by confounding factor detection methods based on eigenvalue gap analysis of the covariance matrix, ensuring robustness and accuracy in gene identification tasks.

Benchmark Development and Experimental Results

To evaluate the TAIS framework, the authors devised the Genetic Question Exploration (GenQEX) dataset, encompassing 457 benchmark questions and a curated gold standard for problem resolution. The authors measured the performance of TAIS using various metrics including precision, recall, and Jaccard index across different regression scenarios. The paper reports favorable results with a success rate of 45.73% across tasks, with variations depending on the complexity of the task (e.g., single-step vs two-step analyses).

Discussion of Implications and Future Directions

The introduction of TAIS provides a noteworthy contribution towards automating genomics research, indicating potential reductions in the manual effort required for complex data analysis processes. Although current efficacy, as reflected in precision and recall, underscores the evolving nature of this technology, the framework establishes a foundational approach that could be iterated upon as LLM capabilities advance.

Theoretical implications of this framework stretch into AI development domains, catalyzing discussions on multi-agent systems' roles within complex scientific research fields. Practically, TAIS marks a step towards more accessible and scalable scientific analyses, enabling researchers to engage with genomic data robustly while focusing their expertise on interpretation and broader biological insights.

Future developments may focus on enhancing the accuracy and reliability of the TAIS framework by integrating more sophisticated machine learning models, iterative learning capabilities, and expanding the framework to encompass broader types of genomic datasets. Additionally, incorporating user feedback mechanisms to fine-tune agent roles and capabilities could bolster performance outcomes further.

In conclusion, while challenges remain in achieving seamless automation of gene expression analysis, the TAIS framework introduces a novel methodological pathway that could significantly impact future AI-driven scientific discovery processes. As researchers continue to expand upon this innovative approach, TAIS could eventually establish itself as a pivotal tool in the genomics toolkit.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown