BioAgents: Bridging the gap in bioinformatics analysis with multi-agent systems
Open accessAbstract
Developing end-to-end bioinformatics workflows is challenging, demanding deep expertise in both genomics and computational techniques. While large language models (LLMs) provide some assistance, they often lack the nuanced guidance required for complex bioinformatics tasks, and are resource-intensive. We thus propose a multi-agent system built on small language models, fine-tuned on bioinformatics data, and enhanced with retrieval augmented generation (RAG). Our system, BioAgents, enables local operation and personalization using proprietary data. We observe performance comparable to human experts on conceptual genomics tasks, and discuss future work to enhance code generation capabilities.
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BioAgents focuses on accessibility by leveraging small language models (SLMs) and retrieval-augmented generation to guide researchers through complex genomics tasks, even without extensive computational backgrounds.
“the BioAgents system, which focuses on accessibility by leveraging small language models (SLMs) and retrieval-augmented generation to guide researchers through complex genomics tasks, even without extensive computational backgrounds [33]”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [33]
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- Authors
- Mehandru, N., Hall, A. K., Melnichenko, O., Dubinina, Y., Tsirulnikov, D., Bamman, D., Alaa, A., Saponas, S., & Malladi, V. S.
- Affiliations
- University of California, Berkeley (US)
- Microsoft (United States) (US)
- University of California, San Francisco (US)
- Corresponding author
- Malladi, Venkat S.
- ORCID
- Venue
- Scientific Reports
- Published
- 2025-11-07
- Volume
- 15
- Issue
- 1
- Pages
- 39036-39036
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC12594986
- OpenAlex
- W4415991135
- Cited by
- 19
- References
- 36
- Citations by year
- 2026: 18 · 2025: 1
- FWCI
- 18.85
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genetics, Bioinformatics, and Biomedical Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- multi-agent systems, bioinformatics pipeline, small language models, retrieval-augmented generation, genomics, local LLM deployment
- MeSH terms
- Humans, Software, Computational Biology, Genomics, Workflow
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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