International Journal Of Bilogical Sciences
.: Home > International Journal Of Bilogical Sciences > 2014 > Volume 10 Number 7 > Lei Yang1,2, Xudong Zhao1, Xianglong Tang1
Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network
Lei Yang1,2, Xudong Zhao1, Xianglong Tang1
1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China; 2. Information and Network Management Centre, Heilongjiang University, Harbin, China. Corresponding author: firstname.lastname@example.org.
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.
predicting disease proteins, clique centrality analysis, association with complex diseases, data integration, protein-protein interaction networks.
Date Deposited : 12 Feb 2016 10:01
Official URL: http://www.ijbs.com/v10i7
Last Modified : 12 Feb 2016 10:01
Volume 10, Number 7, - 2014 , ISSN 1449-2288
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