MRAgent: an LLM-based automated agent for causal knowledge discovery in disease via Mendelian randomization.

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

SUBMITTER: Xu W 

PROVIDER: S-EPMC11975362 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature