通过跨道注意力和分子相互作用信号进行可解释的草药疾病关联预测
Denggao Zheng1, Chi Qin1, Ziyang Wang1
1School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, China.
概括
这项研究介绍了iCAM-Net,这是一个新的可解释模型,通过整合分子相互作用来预测草药疾病关联. 它准确地阐明了传统草药在药物发现中的机制.
科学领域:
- 计算生物学和生物信息学
- 药理学和药物发现
- 传统中国医学的研究.
背景情况:
- 药物发现依赖于确定治疗点,以获得机械的理解.
- 传统的草药在复杂的疾病中提供了协同作用,多组分,多目标的效果.
- 目前的草药疾病协会 (HDA) 预测缺乏分子机制的洞察力.
研究的目的:
- 开发一个可解释的HDA预测模型iCAM-Net.
- 整合分子级相互作用数据,揭示草药机制.
- 提高预测草药疾病关系的准确性.
主要方法:
- 利用双通道架构与超图结构用于草药成分和疾病蛋白质关系.
- 采用跨道注意力机制来捕捉组分蛋白相互作用.
- 将组分蛋白关联 (CPA) 预测作为多任务学习中的辅助任务.
主要成果:
- iCAM-Net实现了高性能,F1分数为0.9611 (TCM套件) 和0.9310 (HERB),AUROC分别为0.9937和0.9779.
- 显著优于基线方法的表现,废弃研究证实了建筑组件的贡献.
- 案例研究验证了Angelica sinensis的预测关联,并通过分子对接确认了成分蛋白相互作用.
结论:
- 通过可解释的HDA预测,iCAM-Net促进了传统医学的现代化.
- 该模型展示了卓越的预测准确性,并阐明了治疗效果的分子机制.
- 源代码是公开可用的,用于进一步的研究和应用.
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