在药物重新定位中,双通路模型的交叉规模的语义融合集成
Mingxuan Li1, Shuai Li1, Zhen Li1
1College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China.
Journal of biomedical informatics
|September 27, 2025
概括
本研究介绍了CSDPDR,这是一种用于药物重新定位的新型双分支图形神经网络. 通过整合拓和突出特征,CSDPDR提高了准确性和效率,优于现有方法.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 药物发现 药物发现 药物发现
背景情况:
- 药物重新定位 (DR) 通过为现有药物找到新的用途,减少成本和时间来加速药物开发.
- 当前的DR方法往往忽略了药物疾病网络中的局部结构细节.
- 图形神经网络具有潜力,但需要对复杂网络分析进行增强.
研究的目的:
- 引入CSDPDR,一种新的双分支图形神经网络,用于改进药物重新定位.
- 为了提高准确性和效率,识别批准药物的新治疗指示.
- 通过结合全球拓和本地突出特征来解决现有方法的局限性.
主要方法:
- 开发了一个双分支图形神经网络 (CSDPDR),集成拓特征信息和突出特征信息.
- 采用了拓意识的分支与自适应的残余图 注意大规模模式.
- 使用 Saliency 意识的分支与得分驱动的 Top-K 卷积图汇集,以获得细粒度的本地信息.
- 使用基于元路径的网络增强和基于信任的过来解决图形稀疏性.
主要成果:
- 在两个基准和一个额外的数据集上,CSDPDR显著超过了几种最先进的基准方法.
- 该模型有效地捕捉了全球拓模式和本地结构细节.
- 在药物重新定位预测中证明了提高准确性和效率.
结论:
- CSDPDR代表了计算药物重新定位的重大进步.
- 该模型能够整合多种特征类型,从而提高预测性能.
- 对阿尔茨海默病和乳腺瘤的案例研究证实了CSDPDR的实际适用性和有效性.
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