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 ...
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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_clusters are overlapping, lambda-modules based on them are also overlapping. Thus, OH-PIN can detect a hierarchical organization of overlappingmodules 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.
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