NLM DIR Seminar Schedule

Abstract:

Gene set analysis allows researchers to explore groups of genes that likely act together in specific biological processes or molecular functions. Recent work in gene set analysis has shown promising performance utilizing large language models (LLMs). Nonetheless, their results are subject to limitations common in LLMs, such as hallucinations. In response, we develop GeneAgent, the first language agent for gene set analysis that self-verifies by autonomously interacting with biological databases, reducing hallucinations and enhancing accuracy. GeneAgent generates novel function names or aligns with notable enriched terms for input gene sets. Benchmarking on 1,106 gene sets from different sources, GeneAgent consistently outperforms vanilla GPT-4 by a significant margin. A detailed manual review confirms the effectiveness of the self-verification module in minimizing hallucinations and generating a more reliable explanatory analysis. We also apply GeneAgent to seven novel gene sets derived from mouse B2905 melanoma cell lines, with expert evaluations showing that GeneAgent offers novel insights into gene functions and expediting knowledge discovery.