通过双阶段注意力和贝叶斯校准与主动学习进行可解释的药物相互作用预测
Rongpei Li1,2, Yufang Zhang1, Heqi Sun1
1State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
本研究介绍了DABI-DDI,这是一个新的计算框架,用于预测药物相互作用 (DDI) 和减少错误阳性. 它通过识别高风险组合和提供生物见解来提高药物安全性.
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
- 生物信息学是一种生物信息学.
背景情况:
- 药物相互作用 (DDI) 在药物不良反应和住院治疗中起着重要作用.
- 现有的DDI预测计算方法具有高的错误阳性率,缺乏生物解释性.
研究的目的:
- 开发一个新的计算框架,DABI-DDI,用于准确和可解释的药物相互作用的预测.
- 解决当前DDI预测模型的局限性,包括错误阳性和缺乏机械洞察力.
主要方法:
- 集成了一个双阶段的注意力机制与LSTM网络进行时间依赖分析.
- 采用贝叶斯校准与β-二项式建模来完善交互信号并减少假阳性.
- 纳入积极学习以进行有效的样本选择和网络药理学以阐明生物机制.
主要成果:
- DABI-DDI表现出优异的预测性能,AUC = 0.947和PR_AUC = 0.944. 这样,DABI-DDI的预测性能更好.
- 贝叶斯校准显著提高了不良事件检测准确性 (94%对54%AUC).
- 网络药理学确定了关键的分子机制,积极学习减少了数据需求.
结论:
- DABI-DDI有效地预测药物相互作用,减少假阳性,并提供生物解释性.
- 该框架通过识别高风险药物组合和阐明潜在途径,提供临床适用性.
- 这种方法弥合了计算预测和临床理解,以获得更安全的药物组合治疗.
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