ArticleBMC bioinformatics2012
An overlapping module identification method in protein-protein interaction networks.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 12 citations in OpenAlex.
- Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies.Malaria journal · 2015Article
- Community structure detection for overlapping modules through mathematical programming in protein interaction networks.PloS one · 2014Article
- Introduction: Advanced intelligent computing theories and their applications in bioinformatics.BMC bioinformatics · 2012Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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