title: Detection of functional modules from protein interaction networks with an enhanced random walk based algorithm.
abstract: In this paper, we propose a new random walk-based clustering algorithm for detecting functional modules in protein-protein interaction (PPI) networks. It has been tested on two yeast PPI networks. Greater precision, better homogeneity and higher modularity were achieved in comparison with the results produced by the recently developed RRW clustering technique and the well-known CFinder algorithm. A much higher level of true positives were observed in the clustering results. The analysis indicated that the proposed method can not only detect overlapping modules but also be potentially used to identify functional modules with different topological structures, which may not be highly connected.
authors.affiliation: Computer Science Research Institute, School of Computing and Mathematics, University of Ulster at Jordanstown, Northern Ireland, BT37 0QB, UK. cai-b@email.ulster.ac.uk
authors.forename: Bingjing
authors.initials: B
authors.lastname: Cai
authors.forename: Haiying
authors.initials: H
authors.lastname: Wang
authors.forename: Huiru
authors.initials: H
authors.lastname: Zheng
authors.forename: Hui
authors.initials: H
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry / Q000378:metabolism
chemical_list: D011506:Proteins