title: Predicting disease-related proteins based on clique backbone in protein-protein interaction network.
abstract: Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.
authors.affiliation: 1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China; ; 2. Information and Network Management Centre, Heilongjiang University, Harbin, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Yang
authors.affiliation: 1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;
authors.forename: Xudong
authors.initials: X
authors.lastname: Zhao
authors.affiliation: 1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;
authors.forename: Xianglong
authors.initials: X
authors.lastname: Tang
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D057225:Data Mining; D005838:Genotype; D008954:Models, Biological; D010641:Phenotype; D025941:Protein Interaction Mapping; D060066:Protein Interaction Maps; D011506:Proteins / Q000378:metabolism; D015398:Signal Transduction
chemical_list: D011506:Proteins
keywords: association with complex diseases; clique centrality analysis; data integration; predicting disease proteins; protein-protein interaction networks.