缓解致病因子用于目标发现和疾病亚型
Eric V Strobl1, Thomas A Lasko2, Eric R Gamazon3
1Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, 1601 23rd Avenue South, Nashville, TN 37232, United States of America.
Computers in biology and medicine
|February 28, 2024
概括
这项研究引入了治疗根因果效应 (TRE),这是一个评估治疗如何改变疾病根源的新指标. TREs可实现治疗目标的自动识别和个性化医疗的患者聚类.
科学领域:
- 因果推断的原因推断是因果推断.
- 生物医学数据分析
- 计算生物学是一种计算生物学.
背景情况:
- 目前的治疗效果定义忽略了致病机制.
- 了解治疗如何影响疾病根源是有效干预的关键.
- 需要指标来量化治疗对潜在疾病病因学的影响.
研究的目的:
- 引入治疗根因果效应 (TRE) 度量来衡量治疗修改的根因果效应.
- 使用TREs开发一种算法,用于自动化治疗目标发现和患者亚型化.
- 在治疗分析中为因果效应估计提供一个可解释的框架.
主要方法:
- 开发了一种部分线性因果模型来提取根源因果效应.
- 估计的治疗根因果效应 (TREs) 用于目标发现和患者亚型.
- 在不假设可逆结构方程模型的情况下确保可解释性.
主要成果:
- 拟议的TRE指标有效量化了治疗对根源因果影响的影响.
- 基于TREs的治疗目标和患者集群的自动识别被证明.
- 该方法在各种数据集中显示了普遍性.
结论:
- 治疗的根源因果效应 (TRE) 通过专注于致病机制,为评估治疗提供了一种新的方法.
- 通过自动目标发现和患者分层,TRE促进了个性化医疗.
- 该方法为因果治疗效应分析提供了一个可解释和广泛适用的框架.
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