Previous studies indicated that many overlapping structures exist among the
modular structures in protein-protein interaction (PPI) networks, which may reflect
common functional components shared by different biological processes. In this paper, a Markov clustering based Link
Clustering (MLC) method for the identification of overlapping modular structures in PPI networks is proposed. Firstly,
MLC method calculates the extended link similarity and derives a similarity matrix to represent the relevance among the
protein interactions. Then it employs markov clustering to partition the link similarity matrix and obtains overlapping
network modules with significantly less parameters and threshold constraints compared to most current methodologies.
Experiments on two networks with known reference classes and two biological PPI networks of Escherichia coli,
Saccharomyces cerevisiae, respectively, show that MLC outperforms the original Link Clustering and the classical Clique
Percolation Method in terms of accurate identification of the core modules in each test network. Therefore, we consider
the MLC method is high promisingly in identifying important pathways through studying the interplay between functional
processes in different organism.
Keywords: Markov clustering, link clustering, overlapping modular, protein-protein interaction.
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