TAPB:一种干预性缓解偏差框架,用于缓解药物向相互作用预测中的目标先导偏差
Gaoming Lin1, Xin Zhang2,3, Zhonghao Ren4,5
1School of Computer Science and Technology, Zhejiang Normal University, Jinhua, Zhejiang, China.
Nature communications
|December 2, 2025
概括
这项研究确定了药物向相互作用预测中的"目标先前偏差",这是由于目标数据不平衡造成的. 一个新的框架,TAPB,使用因果推理来纠正这种偏见,改善药物重定向的模型概括性.
科学领域:
- 计算生物学 计算生物学
- 药理学 药理学是指药理学的学科.
- 机器学习 机器学习
背景情况:
- 药物向相互作用 (DTI) 的预测对于药物重新定位至关重要.
- 现有的DTI模型受到DTI模型的影响.
- 目标先前偏差,目标先前偏差.
- 由于培训数据中的目标标签分布不平衡.
研究的目的:
- 引入一个新的 debiasing 框架,TAPB,以解决 DTI 预测中的目标先前偏差.
- 为了利用因果推理来计算干预概率P(YD,do(T)) 进行准确的DTI建模.
主要方法:
- 氨基酸随机化用于特征表示.
- 混对齐模块 (CAM) 用于偏差缓解.
- 干预训练与后门调整用于因果效应估计.
主要成果:
- TAPB框架有效地减轻了目标先决偏差.
- 与现有的DTI预测方法相比,实现了竞争性表现.
- 演示了增强的模型概括,并提供了可解释的DTI洞察力.
结论:
- 目标先导偏差是DTI预测中的一个重要的混因素.
- TAPB提供了一个强大的解决方案,用于准确和可概括的DTI预测.
- 该框架提高了对药物向相互作用机制的理解.
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