一个可解释的机器学习框架,用于从药物向相互作用中预测药物不良反应
Joseph Roberts-Nuttall1, Alan M Jones2, Marco Castellani1
1School of Mechanical Engineering, University of Birmingham, Edgbaston, United Kingdom.
PloS one
|January 30, 2026
概括
本研究引入了一个可解释的机器学习框架,通过分析药物向相互作用来预测药物不良反应 (ADR). 这种方法提高了药物安全性,并有助于理解ADR机制.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 药物不良反应 (ADRs) 对患者安全和医疗保健构成重大风险.
- 目前的药物监测方法,如黄卡计划 (YCS),缺乏对ADR的机制性见解.
- 可解释机器学习 (ML) 与药物向相互作用数据相结合,为预测和理解ADR提供了一种新的方法.
研究的目的:
- 开发一种可解释的ML框架,使用药物向相互作用数据预测显著的副作用.
- 确定ADRs背后的关键药理学关系.
- 通过预测性药物监测 (PPV) 提高药物安全.
主要方法:
- 综合药物向相互作用数据 (STITCH) 与ADR报告 (YCS).
- 采用不成比例分析来识别ADR信号,用于训练系统器官类别 (SOC) 的随机森林分类器官.
- 使用SMOTE,Tomek和贝叶斯优化来进行数据平衡和超参数调整,其特征对解释性很重要,而DisGeNET则用于验证.
主要成果:
- 在SOC类别中实现了高预测性能,ROC AUC得分高达0.94.
- 通过特征重要性分析确定了药理上相关的药物标.
- 使用DisGeNET验证了发现,并与SIDER相比证明了现实数据的价值.
结论:
- 开发的可解释的ML框架有效地将药物向相互作用与ADR联系起来.
- 这种方法对预测性药监测 (PPV) 具有重大前景.
- 该框架通过提供对ADRs的机制性见解来支持更安全的药物开发.
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