BioRAGent: natural language biomedical querying with retrieval-augmented multiagent systems

作者：Manlian Bi, Zhijie Bao, Dongna Xie, Xiaohan Xie, Changxiao Yang, Tao Wang, Yongtian Wang, Jiajie Peng · 发表于：Briefings in Bioinformatics · 年份：2025 · DOI：10.1093/bib/bbaf539 · 被引用次数：6 · 研究领域：Biomedical Text Mining and Ontologies、Topic Modeling、Natural Language Processing Techniques

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.