Cited by 105Bioinformatics and Genomic NetworksBiomedical Text Mining and OntologiesGenomics and Phylogenetic Studies

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Evaluation of large language models for discovery of gene set function

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Hu, Mengzhou, Alkhairy, Sahar, Lee, Ingoo, Pillich, Rudolf T., Fong, Dylan, Smith, Kevin, Bachelder, Robin, Ideker, Trey, Pratt, Dexter·2025·Nature Methods

DOI 10.1038/s41592-024-02525-xPMC PMC11725441

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Abstract

Gene set enrichment is a mainstay of functional genomics, but it relies on gene function databases that are incomplete. Here we evaluate five large language models (LLMs) for their ability to discover the common functions represented by a gene set, supported by molecular rationale and a self-confidence assessment. For curated gene sets from Gene Ontology, GPT-4 suggests functions similar to the curated name in 73% of cases, with higher self-confidence predicting higher similarity. Conversely, random gene sets correctly yield zero confidence in 87% of cases. Other LLMs (GPT-3.5, Gemini Pro, Mixtral Instruct and Llama2 70b) vary in function recovery but are falsely confident for random sets. In gene clusters from omics data, GPT-4 identifies common functions for 45% of cases, fewer than functional enrichment but with higher specificity and gene coverage. Manual review of supporting rationale and citations finds these functions are largely verifiable. These results position LLMs as valuable omics assistants.

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Authors: Hu, M., Alkhairy, S., Lee, I., Pillich, R. T., Fong, D., Smith, K., Bachelder, R., Ideker, T., & Pratt, D.
Affiliations: University of California San Diego (US)
Corresponding author: Ideker, Trey
Pratt, Dexter
ORCID: Hu, Mengzhou
Alkhairy, Sahar
Lee, Ingoo
Pillich, Rudolf T.
Smith, Kevin
Bachelder, Robin
Ideker, Trey
Pratt, Dexter
Venue: Nature Methods
Publisher: Springer Science and Business Media LLC
Published: 2024-11-28
Volume: 22
Issue: 1
Pages: 82-91
ISSN: 1548-7091 (ISSN-L)
1548-7105
DOI: 10.1038/s41592-024-02525-x
PMC: PMC11725441
OpenAlex: W4404824622
Cited by: 104
References: 90
Citations by year: 2026: 43 · 2025: 52 · 2024: 8
FWCI: 13.98
Type: journal article
Language: en
Open access: green
doi.org
Licence: cc-by
www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Methods (publishedVersion)
Research Square (acceptedVersion, cc-by)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
arXiv (Cornell University) (submittedVersion, cc-by)
eScholarship (California Digital Library) (submittedVersion)
PubMed Central (submittedVersion, cc-by)
arXiv (Cornell University) (cc-by)
Funders: U.S. Department of Health & Human Services | National Institutes of Health (u24ca269436, u01mh115747, u24hg12107, ot2od023742)
Schmidt Futures
Foundation for the National Institutes of Health (u24ca269436)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords: large language models, gene set enrichment analysis, gene function prediction, Gene Ontology, GPT-4, omics data, gene clusters, functional genomics, LLM evaluation

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Evaluation of large language models for discovery of gene set function

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