Evaluation of large language models for discovery of gene set function
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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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Text embeddings integrate omics data with various biological entities such as pathways, functional annotations and cell types, extending the model's capabilities beyond tissue alignment and decomposition, making it adaptable to a broader range of biological tasks.
“First, text embeddings integrate omics data with various biological entities such as pathways, functional annotations68 and cell types69, extending the model’s capabilities beyond tissue alignment and decomposition, making it adaptable to a broader range of biological tasks.”
From A visual–omics foundation model to bridge histopathology with spatial transcriptomics (2025) · cites this paper as [68]
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scConcept enables concept-level exploration of single-cell transcriptomic dataPreprint
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Lense: Optimizing data preprocessing in single-cell omics using LLMsPreprint
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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
- Venue
- Nature Methods
- Published
- 2024-11-28
- Volume
- 22
- Issue
- 1
- Pages
- 82-91
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC11725441
- OpenAlex
- W4404824622
- Cited by
- 104
- References
- 90
- Citations by year
- 2026: 43 · 2025: 52 · 2024: 8 · 2023: 1
- FWCI
- 13.98
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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