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This article is part of the supplement: Advanced intelligent computing theories and their applications in bioinformatics. Proceedings of the 2011 International Conference on Intelligent Computing (ICIC 2011)

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An overlapping module identification method in protein-protein interaction networks

Xuesong Wang*, Lijing Li and Yuhu Cheng

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School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, P. R. China

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Citation and License

BMC Bioinformatics 2012, 13(Suppl 7):S4  doi:10.1186/1471-2105-13-S7-S4

Published: 8 May 2012



Previous studies have shown modular structures in PPI (protein-protein interaction) networks. More recently, many genome and metagenome investigations have focused on identifying modules in PPI networks. However, most of the existing methods are insufficient when applied to networks with overlapping modular structures. In our study, we describe a novel overlapping module identification method (OMIM) to address this problem.


Our method is an agglomerative clustering method merging modules according to their contributions to modularity. Nodes that have positive effects on more than two modules are defined as overlapping parts. As well, we designed de-noising steps based on a clustering coefficient and hub finding steps based on nodal weight.


The low computational complexity and few control parameters prove that our method is suitable for large scale PPI network analysis. First, we verified OMIM on a small artificial word association network which was able to provide us with a comprehensive evaluation. Then experiments on real PPI networks from the MIPS Saccharomyces Cerevisiae dataset were carried out. The results show that OMIM outperforms several other popular methods in identifying high quality modular structures.