DisSubFormer:用于疾病亚图表示和并发症预测的亚图转换器模型
1Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA.
Computational biology and chemistry
|February 8, 2026
概括
DisSubFormer通过从蛋白质-蛋白质相互作用网络和基因本体学信息中学习蛋白质表示来改善疾病并发症预测. 这种新的子图形变压器模型增强了对复杂疾病关系的理解.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 网络医学 网络医学
背景情况:
- 疾病并发症,即多种疾病的同时发生,带来了重大的临床和生物挑战.
- 现有的并发症预测计算方法在模拟碎片化的疾病子图和充分利用基因本体学 (GO) 层次信息方面存在局限性.
研究的目的:
- 开发DisSubFormer,一个新的子图转换器模型,用于增强疾病子图表示学习和并发症预测.
- 改进碎片化的疾病子图的建模,更有效地整合GO的层次结构.
主要方法:
- 通过将蛋白质-蛋白质相互作用 (PPI) 网络结构与GO意识的功能信息集成来学习统一的蛋白质表征,明确纳入GO的等级祖先.
- 采用生物知情补丁和分图到分图的注意力,专注于与疾病相关的PPI网络区域,提高可扩展性和相关性.
- 引入了多头注意力机制,带有头部特定的关系术语,以捕捉碎片化的疾病子图中的多样性.
主要成果:
- 与基准数据集上最先进的方法相比,DisSubFormer在并发症预测方面取得了更好的表现.
- 显示了0.97的接收器运行特征曲线 (AUROC) 下的面积,表明了高的预测准确性.
- 该模型有效地整合了结构和功能信息,利用了政府部门的等级性质.
结论:
- 通过有效地建模复杂的疾病子图形拓,DisSubFormer提供了一种强大的新方法来预测疾病并发症.
- 该方法推进了理解疾病关系的计算策略,并具有潜在的临床应用.
- 集成层次 GO 信息和子图注意力的集成显著提高了预测准确性.
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