title: A graph-based integrative method of detecting consistent protein functional modules from multiple data sources.
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.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Zhang
authors.forename: Yue
authors.initials: Y
authors.lastname: Cheng
authors.forename: Liang
authors.initials: L
authors.lastname: Ge
authors.forename: Nan
authors.initials: N
authors.lastname: Du
authors.forename: Kebin
authors.initials: K
authors.lastname: Jia
authors.forename: Aidong
authors.initials: A
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D030562:Databases, Protein; D008954:Models, Biological; D015233:Models, Statistical; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D018511:Systems Integration
chemical_list: D011506:Proteins