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BIOGEN: evidence-grounded multi-agent reasoning framework for transcriptomic interpretation in antimicrobial

Elias Hossain1, Mehrdad Shoeibi1, Ivan Garibay1

  • 1Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States.

Frontiers in Bioinformatics
|June 15, 2026
PubMed
Summary

BIOGEN, an evidence-grounded framework, enhances RNA sequencing (RNA-seq) interpretation by integrating biomedical data for reliable biological reasoning. It significantly reduces ungrounded outputs, ensuring transparent transcriptomic analysis.

Keywords:
Salmonella entericaantimicrobial resistancebiological reasoninglarge language modelsmulti-agent frameworktranscriptomic interpretation

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Published on: December 9, 2015

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Interpreting gene clusters from RNA sequencing (RNA-seq) is challenging in functional genomics.
  • Mechanistic context is crucial for hypothesis generation in antimicrobial resistance studies.

Purpose of the Study:

  • To present BIOGEN, an evidence-grounded multi-agent framework for post hoc interpretation of RNA-seq transcriptional modules.
  • To improve the reliability and transparency of transcriptomic reasoning.

Main Methods:

  • BIOGEN integrates biomedical retrieval (PubMed, UniProt), structured interpretation, and multi-critic verification.
  • It organizes knowledge into traceable cluster-level explanations with evidence reporting and confidence tiering.

Main Results:

  • BIOGEN achieved strong grounding and biological coherence on a Salmonella enterica dataset (BERTScore 0.689, SAS 0.715).
  • It produced zero ungrounded outputs across five bacterial RNA-seq datasets, outperforming LLM-only and agentic AI baselines.
  • BIOGEN consistently yielded zero non-verifiable identifier outputs across all datasets.

Conclusions:

  • Retrieval access alone is insufficient for reliable biological interpretation.
  • Evidence-grounded orchestration is essential for transparent, source-traceable transcriptomic reasoning, especially under distribution shift.