Abstract
BackgroundPrevious 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.ResultsOur 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.ConclusionsThe 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.
Highlights
Previous studies have shown modular structures in PPI networks
We propose the overlapping module identification method (OMIM), which is able to partition large scale PPI networks with overlapping modular structures
The yeast (Saccharomyces Cerevisiae) PPI networks used in our study are from the MIPS Comprehensive Yeast Genome Database (CYGD) (PPI_18052006) [21]
Summary
Previous studies have shown modular structures in PPI (protein-protein interaction) networks. We describe a novel overlapping module identification method (OMIM) to address this problem. Previous studies have shown that modular structures are densely connected internally but sparsely interacting with others in PPI networks [1,2]. Modules can be understood as independent sub-networks and proteins in the same module always interact more frequently and show stronger functional dependencies. These days, more and more people are likely to address biological problems with graphic models, where proteins or genes are viewed as nodes and their pair wise interactions as edges in a network [3,4]. In 2003, Bader and Hogue proposed a molecular complex detection method (MCODE), which can separate densely connected regions
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