BIOGEN: Evidence-Grounded Multi-Agent Reasoning Framework for Transcriptomic Interpretation in Antimicrobial Resistance
Elias Hossain, Mehrdad Shoeibi, Ivan Garibay, Niloofar Yousefi
Frontiers in Bioinformatics
DOI 10.3389/fbinf.2026.1846404
Summary
BIOGEN is an evidence-grounded multi-agent framework for interpreting RNA-seq gene clusters in antimicrobial resistance research. It combines biomedical retrieval, structured reasoning, and multi-critic verification to generate traceable, cluster-level explanations with explicit evidence and confidence labels.
Abstract
Interpreting transcriptomic gene clusters is challenging in antimicrobial resistance research, where standard pathway enrichment methods often provide limited cluster-specific explanations and weak links to primary literature. BIOGEN addresses this challenge through a multi-agent reasoning framework that integrates biomedical evidence retrieval, structured interpretation, and multi-critic verification. Evaluated on a Salmonella enterica RNA-seq dataset, the framework achieved a BERTScore of 0.689, a Semantic Alignment Score of 0.715, a KEGG Functional Similarity of 0.342, and a hallucination rate of 0.000, compared with 0.100 for an LLM-only baseline. Across five datasets, BIOGEN maintained zero hallucination, highlighting its potential for reliable and evidence-grounded biomedical interpretation.