Automatic biomarker discovery and enrichment with BRAD

Motivation: Integrating Large Language Models (LLMs) with research tools presents technical and reproducibility challenges for biomedical research. While commercial artificial intelligence (AI) systems are easy to adopt, they obscure data provenance, lack transparency, and can generates false information, making them unfit for many research problems. To address these challenges, we developed the Bioinformatics Retrieval Augmented Digital (BRAD) agent software system.

Results: Here, we introduce BRAD, an agentic system that integrates LLMs with external tools and data to streamline research workflows. BRAD’s modular agents retrieve information from literature, custom software, and online databases while maintaining transparent protocols to increase the reliability of AI generated results. We apply BRAD to a biomarker discovery pipeline, automating both execution and the generation of enrichment reports. This workflow contextualizes user data within the literature, enabling a level of interpretation and automation that surpasses conventional research tools. Beyond the workflow we highlight here, BRAD is a flexible system that has been deployed in other applications including a chatbot, video RAG, and analysis of single cell data.

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Recommended BibTeX entry:

@article{pickard2025automatic,
  title={Automatic biomarker discovery and enrichment with {BRAD}},
  author={Pickard, Joshua and Prakash, Ram and Choi, Marc Andrew and Oliven, Natalie and Stansbury, Cooper and Cwycyshyn, Jillian and Galioto, Nicholas and Gorodetsky, Alex and Velasquez, Alvaro and Rajapakse, Indika},
  journal={Bioinformatics},
  volume={41},
  number={5},
  pages={btaf159},
  year={2025},
  publisher={Oxford University Press}
}