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Predicting disease-related proteins based on clique backbone in protein-protein interaction network.

Authors
Type
Published Article
Journal
International Journal of Biological Sciences
1449-2288
Publisher
Ivyspring International Publisher
Publication Date
Volume
10
Issue
7
Pages
677–688
Identifiers
DOI: 10.7150/ijbs.8430
PMID: 25013377
Source
Medline
Keywords
  • Association With Complex Diseases
  • Clique Centrality Analysis
  • Data Integration
  • Predicting Disease Proteins
  • Protein-Protein Interaction Networks.

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.

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