MVSGDR:用于药物重新定位的多视图堆叠图形卷积网络
Guosheng Gu1, Haowei Wu1, Haojie Han1
1School of Computer Science and Technology, Guangdong University of Technology, Waihuan West Road 100, Guangzhou, 510006 Guangdong, China.
这项研究引入了一种新的药物重新定位 (DR) 框架,即MVSGDR,以改善药物与疾病的相关性预测. MVSGDR有效地增强了特征表示和分析关系,优于现有的计算方法.
科学领域:
- 计算生物学
- 药理学
- 网络科学
背景情况:
- 药物重新定位 (DR) 是一种具有成本效益的药物开发策略.
- 目前的计算 DR 方法很难将本地基层模式与全球网络语义结合起来.
- 现有方法通常依赖于数据增强来解决药物疾病关联 (DDA) 中的信息缺口.
研究的目的:
- 提出一个新的DR框架,多视图堆叠图形卷积网络 (MVSGDR),以克服当前计算DR方法的局限性.
- 提高药物疾病关联 (DDA) 的预测准确度.
主要方法:
- 开发了MVSGDR,一个包含三项创新的新型DR框架.
- 多视图堆叠模块通过多跳社区交互的层次聚合来进行深度智能的功能增强.
- 使用METIS分区的子图对DDA进行宽度分析的双层子图转换模块.
- 通过合成负样本减轻样本不平衡的负样本平衡策略.
主要成果:
- 在四个基准数据集的广泛十倍交叉验证实验中,MVSGDR表现出卓越的性能.
- 与现有的DR方法相比,观察到具有统计意义的改善.
- 案例研究成功地通过文献证据确定了以前未报告的DDA.
结论:
- MVSGDR为药物重新定位提供了一个强大的新框架.
- 提出的方法有效地将本地化基层结构模式与全球网络语义相结合.
- MVSGDR显示出发现现有药物的新疗法的巨大潜力.
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