title: Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering.
abstract: Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO) annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP) and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Mailbox 352, 92 West Dazhi Street, Nan Gang District, Harbin 150001, China.
authors.forename: Lei
authors.identifier: 0000-0003-4218-1754
authors.initials: L
authors.lastname: Yang
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Mailbox 352, 92 West Dazhi Street, Nan Gang District, Harbin 150001, China.
authors.forename: Xianglong
authors.initials: X
authors.lastname: Tang
mesh_terms: D030562:Databases, Protein; D008962:Models, Theoretical; D011506:Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*
chemical_list: D011506:Proteins