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Identification of Hierarchical and Overlapping Functional Modules in PPI Networks

机译：PPI网络中分层和重叠功能模块的标识

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摘要

Various evidences have demonstrated that functional modules are overlapping and hierarchically organized in protein-protein interaction (PPI) networks. Up to now, few methods are able to identify both overlapping and hierarchical functional modules in PPI networks. In this paper, a new hierarchical clustering algorithm, called OH-PIN, is proposed based on the overlapping $M_clusters$, $lambda$-module, and a new concept of clustering coefficient between two clusters. By recursively merging two clusters with the maximum clustering coefficient, OH-PIN finally assembles all $M_clusters$ into $lambda$ -modules. Since $M_cluster{rm s}$ are overlapping, $lambda$ -modules based on them are also overlapping. Thus, OH-PIN can detect a hierarchical organization of overlapping modules by tuning the value of $lambda$. The hierarchical organization is similar to the hierarchical organization of GO annotations and that of the known complexes in MIPS. To compare the performance of OH-PIN and other existing competing algorithms, we apply them to the yeast PPI network. The experimental results show that OH-PIN outperforms the existing algorithms in terms of the functional enrichment and matching with known protein complexes.
机译：各种证据表明，功能模块在蛋白质-蛋白质相互作用（PPI）网络中重叠且层次分明。到目前为止，很少有方法能够识别PPI网络中的重叠功能模块和分层功能模块。在本文中，基于重叠的$ M_clusters $，$ lambda $ -module和两个聚类之间的聚类系数的新概念，提出了一种称为OH-PIN的新层次聚类算法。通过递归合并两个聚类系数最大的聚类，OH-PIN最终将所有$ M_clusters $组装成$ lambda $-模块。由于$ M_cluster {rm s} $是重叠的，因此基于它们的$ lambda $-模块也是重叠的。因此，OH-PIN可以通过调整$ lambda $的值来检测重叠模块的层次结构。层次结构类似于GO批注的层次结构以及MIPS中已知复合体的层次结构。为了比较OH-PIN和其他现有竞争算法的性能，我们将其应用于酵母PPI网络。实验结果表明，OH-PIN在功能富集和与已知蛋白复合物的匹配方面优于现有算法。

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