对药物相互作用网络的集群方法进行全面的审查
Shuyuan Cao1,2, Guixia Liu1,2, Xiangrun Zhou1,2
1College of Computer Science and Technology Jilin University Changchun China.
Quantitative biology (Beijing, China)
|February 12, 2026
概括
本综述总结了用于预测药物相互作用 (DDI) 的无监督聚类方法. 这些方法为DDI预测和理解药物机制提供了优势,促进了新的药物组合发现.
科学领域:
- 药理学 药理学是指药理学的学科.
- 生物信息学是一种生物信息学.
- 计算化学计算化学
背景情况:
- 药物相互作用对于安全有效的药物组合至关重要.
- 实验性DDI检测是资源密集的.
- 现有的计算DDI预测审查主要侧重于监督学习方法.
研究的目的:
- 系统地审查用于DDI网络分析的无监督 (集群) 方法.
- 突出DDI预测和行动发现机制的集群方法的优点.
- 为了指导未来的研究在无监督的DDI预测.
主要方法:
- 讨论了用于相似性计算的常见药物信息.
- 引入了基于药物信息和基于网络的聚类算法.
- 描述了用于DDI网络分析的集群评估指标.
主要成果:
- 无监督集群方法为DDI预测的监督方法提供了有价值的替代方案.
- 聚类有助于发现药物相互作用的潜在机制.
- 本综述巩固了关于无监督DDI网络分析的知识.
结论:
- 聚类方法为DDI预测和机制阐明提供了独特的好处.
- 对无监督方法的进一步研究可以促进药物组合发现.
- 这一审查为在DDI网络分析中应用聚类提供了基础.
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