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Updated: Sep 13, 2025

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

GeneAgent: self-verification language agent for gene-set analysis using domain databases.

Zhizheng Wang¹, Qiao Jin¹, Chih-Hsuan Wei¹

¹Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.

|July 28, 2025

Summary

This summary is machine-generated.

GeneAgent, an AI tool, enhances gene-set analysis by reducing factual errors. It autonomously verifies information against biological databases, outperforming existing models like GPT-4 for accurate functional gene descriptions.

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Area of Science:

Bioinformatics
Computational Biology
Artificial Intelligence in Genomics

Background:

Gene-set analysis identifies biological mechanisms for gene groups.
Large language models (LLMs) show potential for gene function description but risk factual inaccuracies (hallucinations).

Purpose of the Study:

To develop and evaluate GeneAgent, an LLM-based AI agent designed to reduce hallucinations in gene-set analysis.
To improve the accuracy and comprehensiveness of functional descriptions for gene sets.

Main Methods:

GeneAgent autonomously interacts with biological databases to verify its generated gene set descriptions.
LLM-based AI agent for gene-set analysis.
Evaluation using 1,106 diverse gene sets and novel gene sets from mouse melanoma cell lines.

Main Results:

GeneAgent demonstrated significantly higher accuracy compared to GPT-4 across 1,106 evaluated gene sets.
Expert review confirmed GeneAgent's superior relevance and comprehensiveness for novel gene sets.
GeneAgent effectively reduces hallucinations in LLM-generated functional descriptions.

Conclusions:

GeneAgent offers a more accurate and reliable approach to gene-set analysis compared to existing LLMs.
This AI agent expedites biological knowledge discovery by providing validated functional insights.
GeneAgent represents a significant advancement in applying AI for biological mechanism identification.