推进基于边缘的聚类和图形嵌入用于生物网络分析:RASopathies中的一个案例研究
Federico García-Criado1, Pedro Seoane1,2,3, Elena Rojano1,2,3
1Department of Molecular Biology and Biochemistry, University of Malaga, 29010 Malaga, Spain.
Briefings in bioinformatics
|July 7, 2025
概括
我们开发了一种新的网络嵌入方法,它集成了层次链接集群 (HLC) 来更好地表示复杂的生物网络. 这种方法增强了在蛋白质-蛋白质相互作用 (PPI) 网络中发现功能关系和潜在的疾病基因候选者的发现.
科学领域:
- 计算生物学 计算生物学
- 网络科学 网络科学
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白与蛋白相互作用 (PPI) 网络对于理解生物过程至关重要.
- 现有的网络表示方法往往无法捕捉生物系统固有的重叠模块化结构.
- 准确的网络表示对于生物解释性和预测能力至关重要.
研究的目的:
- 提出一种新的网络嵌入策略,通过解决传统集群方法的局限性来提高生物解释性和预测能力.
- 将层次链接集群 (HLC) 集成到一个网络中,嵌入大型,加权,无定向网络的工作流.
- 为了增强生物通路的表现,并确定新型基因候选人,如RASopathies等疾病.
主要方法:
- 在Python和R中开发了优化的层次链接集群 (HLC) 实现,以提高准确性和可扩展性.
- 将HLC集成到网络中,通过将随机步行限制在HLC定义的社区中,嵌入工作流.
- 应用集群嵌入工作流程来使用Reactome途径分析人类PPI网络并调查RASopathies.
主要成果:
- 与现有方法相比,优化的HLC实现显示出更高的集群精度和可扩展性.
- 将随机步行限制在HLC定义的社区中,改善了人类PPI网络中的生物途径的表现.
- 集群嵌入工作流成功识别了与RASopathies相关的潜在新型基因候选者,包括Noonan和Costello综合征.
结论:
- 将层次链接集群 (HLC) 与网络嵌入集成,可以提供更具生物解释性和预测性的复杂PPI网络的表示.
- 这种方法增强了生物通路的分析,并促进了新型疾病相关基因的发现.
- 开发的HLC实现和嵌入工作流程为网络生物学研究提供了宝贵的工具.
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