ArticleBioinformatics (Oxford, England)2025
Automatic biomarker discovery and enrichment with BRAD.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
10 citing papers in PubMed.
- Article
- Trustworthy Agentic AI in Bioinformatics: From Workflow Automation to Traceable and Validated Biological Inference.Biology · 2026Review
- ELISA (Embedding-Linked Interactive Single-cell Agent): an interpretable hybrid generative Artificial Intelligence agent for expression-grounded discovery in single-cell genomics.Briefings in bioinformatics · 2026Article
- Explainable Agentic Artificial Intelligence in Healthcare: A Scoping Review.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial Intelligence agents for biological research: a survey.Briefings in bioinformatics · 2026Article
- Building MCP-native hierarchical AI scientist ecosystems: a perspective on scaling multi-agent scientific discovery.Frontiers in artificial intelligence · 2026Article
- Large Language Models for Accessible Reporting of Bioinformatics Analyses in Interdisciplinary Contexts.bioRxiv : the preprint server for biology · 2025Article
- Streamline automated biomedical discoveries with agentic bioinformatics.Briefings in bioinformatics · 2025Review
- AI Agents in Clinical Medicine: A Systematic Review.medRxiv : the preprint server for health sciences · 2025Article
- A conceptual framework for human-AI collaborative genome annotation.Briefings in bioinformatics · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
motivationIntegrating 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.
resultsHere, 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. AVAILABILITY AND IMPLEMENTATION: The source code for BRAD is available at https://github.com/Jpickard1/BRAD; Information for pip installation, tutorials, documentation, and further information can be found at: ReadTheDocs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.