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WeCoMXP: Weighted Connectivity Measure Integrating Co-Methylation, Co-Expression and Protein-Protein Interactions for Gene-Module Detection.
IEEE/ACM Transactions on Computational Biology and Bioinformatics ( IF 4.5 ) Pub Date : 2018-09-03 , DOI: 10.1109/tcbb.2018.2868348
Saurav Mallik , Sanghamitra Bandyopadhyay

The identification of modules (groups of several tightly interconnected genes) in gene interaction network is an essential task for better understanding of the architecture of the whole network. In this article, we develop a novel weighted connectivity measure integrating co-methylation, co-expression and protein-protein interactions (called WeCoMXP) to detect gene-modules for multi-omics dataset. The proposed measure goes beyond the fundamental degree centrality measure through considering some formulation of higher-order connections. Thereafter, we apply the average linkage clustering method using the corresponding dissimilarity (distance) values of WeCoMXP scores, and utilize a dynamic tree cut method for identifying some gene-modules. We validate the modules through literature search, KEGG pathway, and gene-ontology analyses on the genes representing the modules. Furthermore, the top ten TFs/miRNAs that are connected with the maximum number of gene-modules and that regulate/target the maximum number of genes from these connected gene-modules, are identified. Moreover, our proposed method provides a better performance than the existing methods in terms of several cluster-validity indices in maximum times.

中文翻译:

WeCoMXP:加权连通性度量,集成了共甲基化,共表达和蛋白质-蛋白质相互作用,用于基因模块检测。

基因相互作用网络中模块(几个紧密相关基因的组)的识别是更好地了解整个网络体系结构的一项基本任务。在本文中,我们开发了一种新颖的加权连通性度量,该度量集成了共甲基化,共表达和蛋白质-蛋白质相互作用(称为WeCoMXP)来检测多组学数据集的基因模块。通过考虑一些高阶连接的公式,所提出的度量超出了基本度中心度度量。此后,我们使用WeCoMXP分数的相应相异性(距离)值应用平均连锁聚类方法,并利用动态树切割方法来识别某些基因模块。我们通过文献搜索,KEGG途径,对代表模块的基因进行基因本体分析。此外,鉴定了与最大数量的基因模块连接并且调节/靶向来自这些连接的基因模块的最大基因数量的前十个TF / miRNA。此外,就最大次数上的几个聚类有效性指标而言,我们提出的方法提供了比现有方法更好的性能。
更新日期:2020-04-22
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