VHGAE:基于异质图变量自编码器的药物向相互作用预测模型
Chen Zhang1, Jiaqi Sun1, Linlin Xing2
1Computer Science and Technology, Shandong University of Technology, Zibo, 255000, China.
Interdisciplinary sciences, computational life sciences
|August 21, 2025
概括
预测药物向相互作用 (DTI) 对于药物发现至关重要. 一种新的方法,VHGAE,有效地解决了稀疏的网络挑战,以提高DTI预测的准确性.
科学领域:
- 计算生物学
- 生物信息学
- 网络科学
背景情况:
- 药物向相互作用 (DTI) 的识别对于药物发现和重新定位至关重要.
- 传统的生物识别方法耗时且昂贵.
- 异质网络方法为DTI预测提供了更快的方法,但已知的DTI数据的稀疏性对图形卷积网络构成了挑战.
研究的目的:
- 提出VHGAE,一种基于异质图变异自编码器的新方法,用于准确预测药物向相互作用.
- 解决DTI预测的异质网络中的数据稀疏性问题.
- 利用多个来源的先前知识来提高DTI预测.
主要方法:
- 通过整合有关药物和目标的各种先验知识,构建了一个异质的网络.
- 应用加权的k-最近邻近算法来加密药物目标交互网络,增强节点连接性.
- 在变量图自编码器框架内使用加权图卷积网络来加强边缘权重.
- 整合了一个变化期望最大化算法来恢复稀疏网络中的潜在关系.
主要成果:
- 拟议的VHGAE方法在预测药物向相互作用方面表现出卓越的性能.
- 在两个基准数据集上,VHGAE的表现优于现有的九种最先进的DTI预测方法.
- 这些结果突显了VHGAE在多源数据融合和稀疏网络处理方面的有效性.
结论:
- 通过有效处理稀疏的异质网络,VHGAE显著提高了药物向相互作用预测的准确性.
- 该方法集成多源数据和处理稀疏网络的能力是其增强性能的关键.
- VHGAE提供了一个有前途的计算方法来加速药物发现和重新定位的努力.
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