MRAgent:一种基于LLM的自动化代理,用于通过孟德尔随机化在疾病中发现因果知识
Wei Xu1, Gang Luo1, Weiyu Meng1
1Centre for Artificial Intelligence Driven Drug Discovery, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macao SAR 999078, China.
Briefings in bioinformatics
|April 7, 2025
概括
门德尔随机化 (MR) 使用遗传数据识别因果关系,但需要预定义的对. 使用大型语言模型 (LLM) 的自动化代理商MRAgent从文献中发现了这些对,以便在疾病研究中有效地推断因果关系.
科学领域:
- 生物医学信息学 生物医学信息学
- 遗传学 是一个遗传学.
- 流行病学 流行病学
背景情况:
- 因果推断对于医学研究,干预开发和诊断至关重要.
- 门德尔随机化 (MR) 是推断因果关系的一个关键遗传方法.
- 确定MR的暴露-结果对是当前研究中的一个重大瓶.
研究的目的:
- 引入MRAgent,一个自动化代理,旨在增强疾病研究中的因果知识发现.
- 利用大型语言模型 (LLM) 来自主识别潜在的暴露-结果对.
- 通过自动化发现过程来促进门德尔随机化 (MR) 的因果推理.
主要方法:
- 开发了MRAgent,一种使用LLM扫描科学文献的自动化代理.
- 实施了与疾病研究相关的潜在暴露-结果对的自主发现.
- 集成的MRAgent与全基因组关联研究 (GWAS) 数据用于MR因果推断.
主要成果:
- 从科学文献中证明了MRAgent自主识别暴露-结果对的能力.
- 通过使用发现的对和GWAS数据成功执行了MR因果推断.
- 通过自动化和人为评估,评估MRAgent内部的LLM绩效.
结论:
- 通过自动化文献扫描和MR分析,MRAgent显著提升了因果发现.
- 该工具为研究人员和临床医生提供了一种强大的方法,用于探索复杂疾病中的因果关系.
- 自动化工作流简化了因果因素的识别和验证,加速了医学研究.
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