Evaluation of large language models for discovery of gene set function
This addresses the problem of incomplete curated databases in functional genomics for researchers, offering an incremental improvement by applying existing LLMs to a new domain.
The study evaluated five large language models for discovering common biological functions of gene sets, finding that GPT-4 recovered curated names or general concepts in 73% of cases and identified novel functions in 32% of cases from 'omics data.
Gene set analysis is a mainstay of functional genomics, but it relies on curated databases of gene functions that are incomplete. Here we evaluate five Large Language Models (LLMs) for their ability to discover the common biological functions represented by a gene set, substantiated by supporting rationale, citations and a confidence assessment. Benchmarking against canonical gene sets from the Gene Ontology, GPT-4 confidently recovered the curated name or a more general concept (73% of cases), while benchmarking against random gene sets correctly yielded zero confidence. Gemini-Pro and Mixtral-Instruct showed ability in naming but were falsely confident for random sets, whereas Llama2-70b had poor performance overall. In gene sets derived from 'omics data, GPT-4 identified novel functions not reported by classical functional enrichment (32% of cases), which independent review indicated were largely verifiable and not hallucinations. The ability to rapidly synthesize common gene functions positions LLMs as valuable 'omics assistants.