ArticleNature methods2025
GeneAgent: self-verification language agent for gene-set analysis using domain databases.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Proteomic and machine learning analysis predicts treatment response signatures in Myasthenia Gravis.Journal of translational medicine · 2026Trial
- MAPA: A Semantic Network Framework for Functional Module Discovery and Interpretation in Multi-Omics Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Characterizing highly conserved fragments in 3'UTRs via statistical and transfer learning approaches.NAR genomics and bioinformatics · 2026Article
- ELISA (Embedding-Linked Interactive Single-cell Agent): an interpretable hybrid generative Artificial Intelligence agent for expression-grounded discovery in single-cell genomics.Briefings in bioinformatics · 2026Article
- Cell surface CD11c as a neutrophil aging marker molecule.FEBS open bio · 2026Article
- CompBio and MIRaS-a multi-omic analysis platform built on a memory-based intelligence engine.Nucleic acids research · 2026Article
- IAN, an intelligent system for omics data analysis and discovery.Cell reports methods · 2026Article
- Agentic genomics: From pipeline automation to autonomous validation.Cell genomics · 2026Review
- GeneInsight: Condensing gene set knowledge via language models.PLoS computational biology · 2026Article
- An embedding-based framework enables statistical testing of gene-set function hypotheses inferred by large language models.Nature communications · 2026Article
- Tutorial: guidance on the use of large language models for medical research.Nature protocols · 2026Review
- MechAInistic: A Reviewer-Supervised Multi-Agent LLM System for Auditable Mechanistic Drug-Hypothesis Generation.bioRxiv : the preprint server for biology · 2026Article
- Empowering biomedical evidence exploration and synthesis with deep knowledge graph research.Nature machine intelligence · 2026Article
- Knowledge-guided contextual gene set analysis with large language models.Bioinformatics (Oxford, England) · 2026Article
- A comprehensive survey of AI agents in healthcare.Journal of biomedical informatics · 2026Review
- Natural Resistance to Ovarian Hyperstimulation Syndrome in Estrildid Finches Reveals Macrophage GPR183 as a Potential Therapeutic Target.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- An AI-Powered Trisomy 21 Research Assistant.bioRxiv : the preprint server for biology · 2026Article
- SpikeLab: Agentic tools for spike data analysis.bioRxiv : the preprint server for biology · 2026Article
- EcoRxAgent: an AI agent for generating economically substitutable prescriptions.NPJ digital medicine · 2026Article
- MetaScreener: a robust dual-mode framework for directional prioritization of actionable signatures through multi-dataset and multi-approach integration.Journal of translational medicine · 2026Article
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Authors and funding
10 authors.
Funding
Abstract
Gene-set analysis seeks to identify the biological mechanisms underlying groups of genes with shared functions. Large language models (LLMs) have recently shown promise in generating functional descriptions for input gene sets but may produce factually incorrect statements, commonly referred to as hallucinations in LLMs. Here we present GeneAgent, an LLM-based AI agent for gene-set analysis that reduces hallucinations by autonomously interacting with biological databases to verify its own output. Evaluation of 1,106 gene sets collected from different sources demonstrates that GeneAgent is consistently more accurate than GPT-4 by a significant margin. We further applied GeneAgent to seven novel gene sets derived from mouse B2905 melanoma cell lines. Expert review confirmed that GeneAgent produces more relevant and comprehensive functional descriptions than GPT-4, providing valuable insights into gene functions and expediting knowledge discovery.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.