A graph-based integrative method of detecting consistent protein functional modules from multiple data sources. (2015)

• Record Type: Journal Article
• Authors: Zhang, Yuan; Cheng, Yue; Ge, Liang; Du, Nan; Jia, Kebin; Zhang, Aidong
• Is Part Of: International journal of data mining and bioinformatics. Volume 13:Number 2(2015)
• Page Start: 122
• Page End: 140
• Publication Date: 2015
• Subjects: PPI networks -- protein-protein interaction -- multiple data sources -- biological data integration -- consensus mining -- functional module detection -- non-negative matrix factorisation -- bioinformatics -- protein functional similarity -- bipartite graphs; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

Many clustering methods have been developed to identify functional modules in Protein-Protein Interaction (PPI) networks but the results are far from satisfaction. To overcome the noise and incomplete problems of PPI networks and find more accurate and stable functional modules, we propose an integrative method, bipartite graph-based Non-negative Matrix Factorisation method (BiNMF), in which we adopt multiple biological data sources as different views that describe PPIs. Specifically, traditional clustering models are adopted as preliminary analysis of different views of protein functional similarity. Then the intermediate clustering results are represented by a bipartite graph which can comprehensively represent the relationships between proteins and intermediate clusters and finally overlapping clustering results are achieved. Through extensive experiments, we see that our method is superior to baseline methods and detailed analysis has demonstrated the benefits of integrating diverse clustering methods and multiple biological information sources.