Large language models (LLMs) demonstrate potential as assistants in functional genomics, offering a new avenue for gene set analysis. In our evaluation of five LLMs, GPT-4 was the top-performing model and generated common functions for gene sets with high specificity, reliable self-assessed confidence and supporting analysis, complementing traditional functional enrichment.
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References
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA. 102, 15545–15550 (2005). This paper presents the gene set enrichment analysis tool, providing one example of functional enrichment methods.
Joachimiak, M. P., Harry Caufield, J., Harris, N. L., Kim, H. & Mungall, C. J. Gene set summarization using large language models. Preprint at https://arxiv.org/abs/2305.13338 (2023). A preprint that introduces the use of LLMs to retrieve relevant GO terms to annotate the gene set.
Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000). This article presents the GO consortium, a project to annotate genes using a common vocabulary across different organisms.
Huang, L. et al. A survey on hallucination in large language models: principles, taxonomy, challenges, and open questions. Preprint at https://arxiv.org/abs/2311.05232 (2023). This preprint reviews ‘hallucinations’ seen in emerging LLMs and guides the approach to detect and mitigate this phenomenon.
Wang, Z. et al. GeneAgent: self-verification language agent for gene set knowledge discovery using domain databases. Preprint at https://arxiv.org/abs/2405.16205 (2024). This preprint extends our study by building a pipeline that reduces ‘hallucinations’ and improves reliability by autonomously interacting with biological databases.
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This is a summary of: Hu, M. et al. Evaluation of large language models for discovery of gene set function. Nat. Methods https://doi.org/10.1038/s41592-024-02525-x (2024).
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Enhancing functional gene set analysis with large language models. Nat Methods 22, 22–23 (2025). https://doi.org/10.1038/s41592-024-02526-w
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DOI: https://doi.org/10.1038/s41592-024-02526-w
