Int J Biol Sci 2014; 10(7):677-688. doi:10.7150/ijbs.8430 This issue

Research Paper

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.

This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) License. See for full terms and conditions.
Yang L, Zhao X, Tang X. Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network. Int J Biol Sci 2014; 10(7):677-688. doi:10.7150/ijbs.8430. Available from

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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.

Keywords: predicting disease proteins, clique centrality analysis, association with complex diseases, data integration, protein-protein interaction networks.