Authors
Wendi Wei, Yuling Fan, Lin Li, Thuc Duy Le, Xiaomei Li, Debo Cheng, Zaiwen Feng
Published in
Bioinformatics (Oxford, England). Oct 19, 2026. Epub Oct 19, 2026.
Abstract
Understanding disease-related causal mechanisms is essential for biomedical research, yet the practical use of Mendelian Randomization (MR) remains constrained by fragmented analytical workflows and a strong dependence on specialized expertise. These challenges limit the efficient, standardized, and reproducible execution of end-to-end MR analyses.
We propose MR-MAS, an end-to-end automated MR analysis system built on a multi-agent architecture. MR-MAS leverages the reasoning and coordination capabilities of large language models (LLMs) and integrates external tools to unify data acquisition, causal effect estimation, results interpretation, and manuscript preparation into a single workflow, thereby reducing manual effort. MR-MAS further incorporates a structured Mendelian Randomization Knowledge Base (MRKB) to support MR method implementation and targeted repair of MR analysis code. Across diverse test scenarios, MR-MAS demonstrated stable workflow execution, reproduced several literature-supported causal signals, and maintained consistent causal estimates across repeated runs and different model backends. Additional evaluations showed robust trait retrieval and improved code-repair performance with MRKB guidance. These results suggest that MR-MAS may provide practical support for scalable, standardized, and reproducible MR analyses in biomedical research.
The source code, MRKB, and benchmark data are freely available at https://github.com/Zaiwen/MR-MAS, with the version used in this study archived at https://doi.org/10.5281/zenodo.20342636.
Supplementary data are available at Bioinformatics online.
PMID:
42895113
Bibliographic data and abstract were imported from PubMed on 19 Oct 2026.
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