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活动模块识别算法的评估和聚合

Jason Liu^1,2, Min Xu³, Jinchuan Xing^1,2

¹Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.

bioRxiv : the preprint server for biology

|November 24, 2025

概括

对基因与基因相互作用网络的活性模块识别 (AMI) 算法进行评估,发现没有一种方法在所有数据集中脱而出. 建议将来自多个算法的输出结合起来,使用光谱聚类或基于贪行为的合并 (GCM),以进行全面的生物信号捕获.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
系统生物学系统生物学

背景情况:

高通量测序产生了大量的遗传数据,用于候选基因研究.
基因相互作用 (GGI) 网络对于分析候选基因至关重要,但可能变得复杂.
积极模块识别 (AMI) 对于通过寻找丰富的子网络来降低GGI网络复杂性至关重要.

研究的目的:

为了全面评估和比较四个AMI算法的性能:PAPER,DOMINO,FDRnet和HotNet2.
评估这些算法在GGI网络中识别特定环境的生物丰富的能力.
提出将多个AMI算法的结果汇总的方法,以增强生物信号检测.

主要方法:

经验管道 (EMP) 用于评估四个AMI算法在四个不同的生物数据集.
基于算法产生特定上下文丰富的能力来评估性能.
开发了两种新的聚合方法:光谱聚类和基于贪行为的合并 (GCM).

主要成果:

没有单一的AMI算法在所有测试的生物数据集中表现出卓越的性能.
不同的AMI算法的输出通常不相似,表明它们捕获了互补的生物信息.
提出的聚合方法 (光谱聚类和GCM) 可以有效地结合来自多个算法的输出.

结论:

使用AMI对GGI网络进行全面分析需要整合多个算法的结果.
开发的工具和工作流程增强了使用AMI算法的研究人员的分析能力.
自由可用的代码和资源促进了这些方法的更广泛的采用和应用.

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