title: Evolutionary Graph Clustering for Protein Complex Identification.
abstract: This paper presents a graph clustering algorithm, called EGCPI, to discover protein complexes in protein-protein interaction (PPI) networks. In performing its task, EGCPI takes into consideration both network topologies and attributes of interacting proteins, both of which have been shown to be important for protein complex discovery. EGCPI formulates the problem as an optimization problem and tackles it with evolutionary clustering. Given a PPI network, EGCPI first annotates each protein with corresponding attributes that are provided in Gene Ontology database. It then adopts a similarity measure to evaluate how similar the connected proteins are taking into consideration the network topology. Given this measure, EGCPI then discovers a number of graph clusters within which proteins are densely connected, based on an evolutionary strategy. At last, EGCPI identifies protein complexes in each discovered cluster based on the homogeneity of attributes performed by pairwise proteins. EGCPI has been tested with several real data sets and the experimental results show EGCPI is very effective on protein complex discovery, and the evolutionary clustering is helpful to identify protein complexes in PPI networks. The software of EGCPI can be downloaded via: https://github.com/hetiantian1985/EGCPI.
authors.forename: Tiantian
authors.initials: T
authors.lastname: He
authors.forename: Keith C C
authors.initials: KCC
authors.lastname: Chan
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D025941:Protein Interaction Mapping / Q000379:methods*; D060066:Protein Interaction Maps / Q000502:physiology*; D011506:Proteins / Q000737:chemistry / Q000378:metabolism / Q000502:physiology; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry / Q000378:metabolism / Q000502:physiology
chemical_list: D011506:Proteins; D029701:Saccharomyces cerevisiae Proteins