ArticleBriefings in bioinformatics2025
MRAgent: an LLM-based automated agent for causal knowledge discovery in disease via Mendelian randomization.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- MR-MAS: an end-to-end multi-agent system for autonomous Mendelian randomization analysis.Bioinformatics (Oxford, England) · 2026Article
- TCM-Agent: Advancing network pharmacology and herbal medicine discovery with LLM-based multi-agent systems.Journal of pharmaceutical analysis · 2026Article
- ClinPreAI: An Agentic AI System for Early Postpartum Depression Risk Prediction from Multimodal EHR Data.medRxiv : the preprint server for health sciences · 2026Article
- Large language model agents for biological intelligence across genomics, proteomics, spatial biology, and biomedicine.Briefings in bioinformatics · 2026Review
- Artificial Intelligence agents for biological research: a survey.Briefings in bioinformatics · 2026Article
- Large language model-driven agents in nursing practice: A scoping review.International journal of nursing sciences · 2025Review
- Causal Inference Framework Reveals Mediterranean Diet Superiority and Inflammatory Mediation Pathways in Mortality Prevention: A Comparative Analysis of Nine Common Dietary Patterns.Foods (Basel, Switzerland) · 2025Article
- AI Agents in Clinical Medicine: A Systematic Review.medRxiv : the preprint server for health sciences · 2025Article
- Multiple Confabulations Found in Bioinformatics Tasks Carried Out by Several Free Large Language Models.Current genomics · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding causality in medical research is essential for developing effective interventions and diagnostic tools. Mendelian Randomization (MR) is a pivotal method for inferring causality through genetic data. However, MR analysis often requires pre-identification of exposure-outcome pairs from clinical experience or literature, which can be challenging to obtain. This poses difficulties for clinicians investigating causal factors of specific diseases. To address this, we introduce MRAgent, an innovative automated agent leveraging Large Language Models (LLMs) to enhance causal knowledge discovery in disease research. MRAgent autonomously scans scientific literature, discovers potential exposure-outcome pairs, and performs MR causal inference using extensive Genome-Wide Association Study data. We conducted both automated and human evaluations to compare different LLMs in operating MRAgent and provided a proof-of-concept case to demonstrate the complete workflow. MRAgent's capability to conduct large-scale causal analyses represents a significant advancement, equipping researchers and clinicians with a robust tool for exploring and validating causal relationships in complex diseases. Our code is public at https://github.com/xuwei1997/MRAgent.
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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.