深度同类异质的关联挖掘与混合学者和多维混合时刻网络:嵌入驱动的微生物药物相互作用的预测
Yiming Chen1, Hao Li2, Lei Wang1
1College of Computer Science & Engineering, Changsha University, Changsha, 410022, China.
Computers in biology and medicine
|July 11, 2025
概括
我们开发了GSSMMT,这是一个用于预测药物-微生物关联 (MDA) 的新计算框架. 这种方法通过提高预测准确性和可解释性来增强微生物疗法药物重新用途.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 微生物学 微生物学
背景情况:
- 药物重新利用加速了微生物疗法的发展.
- 目前用于预测药物-微生物关联 (MDA) 的计算方法在捕获复杂数据方面存在局限性,并且缺乏可解释性.
研究的目的:
- 提出一个新的框架,学术指导和多维时刻神经网络 (GSSMMT),用于预测药物-微生物关联 (MDA).
- 提高药物重用MDA预测的准确性和机械解释性.
主要方法:
- 将生物医学数据整合到药物和微生物的同质图表中.
- 采用多视图随机步行并构建一个异质网络.
- 采用了双路径架构,结合了学者引导网络和多维时刻神经网络.
- 应用交叉图形融合与基于注意力的转位矩阵和双塔支向量机器进行预测.
主要成果:
- 在三个基准数据集上,GSSMMT的表现优于五种最先进的方法.
- 废弃性研究验证了GSSMMT组件的有效性.
- 案例研究显示,超过80%的临床相关药物与PubMed报告的药物微生物相互作用一致.
结论:
- GSSMMT提供了一种可靠和可解释的方法来识别潜在的药物-微生物关联.
- 该框架推进了微生物疗法开发中药物重新利用的计算策略.
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