ArticleBriefings in bioinformatics2025
BioRAGent: natural language biomedical querying with retrieval-augmented multiagent systems.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- GOLDEN fusion: a graph-oriented learning with domain-embedding network fusion for generating super gene sets in functional genomics.Briefings in bioinformatics · 2026Article
- MedHopper: an agentic RAG-LLM system for multi-hop biomedical QA.Database : the journal of biological databases and curation · 2026Article
- IFNg_DeepKG: A Novel Model for Identifying Interferon-Gamma-Inducing Epitopes Using Knowledge Graph RAG in Biomedical Applications.Journal of chemical information and modeling · 2026Article
- Beyond text: quantifying the impact of figure-guided retrieval in biomedical RAG systems.Frontiers in artificial intelligence · 2026Article
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Authors and funding
8 authors.
Funding
Abstract
Understanding the roles of genes, phenotypes, and diseases is crucial for advancing biomedical research. However, efficient and accessible retrieval of biomedical knowledge remains a challenge due to the complexity of the relevant data. We introduce BioRAGent, an intelligent biomedical assistant that combines Tool-augmented retrieval-augmented generation (RAG) with a multiagent system. Leveraging the ability of large language models, BioRAGent facilitates natural language queries about genes, phenotypes, diseases, and their interrelationships. BioRAGent employs three specialized agents: Guide (query optimization), Retriever (data retrieval), and Reviewer (answer validation) to access authoritative biomedical databases and to generate accurate responses. We evaluate the performance of BioRAGent on a benchmark of eleven single-hop and three multi-hop tasks, demonstrating superior results compared with state-of-the-art models. User evaluations highlight the practicality and robust user experience of BioRAGent, particularly in handling complex multi-hop queries. Moreover, ablation experiments validate the contribution of each agent in improving retrieval accuracy.
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Registered trials
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.