一种基于GAN的两阶段仪表变量方法,用于对omics数据的因果分析
Yuan Zhou1, Pei Geng2, Shan Zhang3
1Department of Biostatistics, University of Florida, Gainesville, FL 32611, United States.
Briefings in bioinformatics
|February 23, 2026
概括
我们介绍了一个新的深度学习框架,GAN-IV,用于孟德尔随机化 (MR) 分析. 这种方法准确地估计了从基因表达到疾病的因果关系,超过了复杂遗传研究中的现有方法.
科学领域:
- 遗传学和生物信息学 遗传学和生物信息学
- 因果推理因果推理
- 机器学习 机器学习
背景情况:
- 鉴定复杂疾病的因果基因是具有挑战性的.
- 门德尔随机化 (MR) 使用遗传变异推断因果关系,但面临来自违反假设和非线性方面的偏见.
- 现有的MR方法与复杂的奥米克数据和未观察到的混因素作斗争.
研究的目的:
- 为MR分析开发一个强大的,无分发的深度学习框架.
- 解决MR中违反仪器变量假设和非线性暴露结果关系的情况.
- 为了使复杂,多omics数据的因果推断.
主要方法:
- 一个两阶段的深度学习框架,利用生成对抗网络 (GAN) 和深度功能神经网络.
- 阶段1:GAN估计给定基因变异 (IVs) 的条件基因表达分布.
- 第二阶段:深度功能网络模拟基因表达与疾病结果之间的非线性因果关系.
主要成果:
- 拟议的基于GAN的仪表变量 (GAN-IV) 方法在模拟中显示出优于传统和基于深度学习的MR方法的性能.
- GAN-IV有效地捕获复杂的非线性因果效应,并处理各种各样的数据类型.
- 在ROSMAP数据集上实时数据的应用证实了GAN-IV模拟基因表达-疾病表型关系的能力.
结论:
- GAN-IV提供了一个强大的,没有分布的工具,用于复杂的奥米克数据中的因果推理.
- 该框架解释了未观察到的类型和链接不平衡,改善了因果效应估计.
- 这种方法有助于准确识别与疾病相关的基因及其病因作用.
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