An overlapping module identification method in protein-protein interaction networks
BMC Bioinformatics, ISSN: 1471-2105, Vol: 13, Issue: SUPPL.7, Page: S4
2012
- 26Citations
- 27Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations26
- Citation Indexes26
- 26
- CrossRef4
- Captures27
- Readers27
- 27
Article Description
Background: 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.Results: 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.Conclusions: 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. © 2012 Wang et al.; licensee BioMed Central Ltd.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84872764785&origin=inward; http://dx.doi.org/10.1186/1471-2105-13-s7-s4; http://www.ncbi.nlm.nih.gov/pubmed/22595001; https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-13-S7-S4; http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-13-S7-S4; https://dx.doi.org/10.1186/1471-2105-13-s7-s4
Springer Nature
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