ArticleInternational journal of biological sciences2014
Predicting disease-related proteins based on clique backbone in protein-protein interaction network.
Article in International journal of biological sciences, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 20 citations in OpenAlex.
- Exploring the biological functions of PCOS: identifying hub androgen-related genes through bioinformatics.Frontiers in medicine · 2026Article
- Cross-modal embedding integrator for disease-gene/protein association prediction using a multi-head attention mechanism.Pharmacology research & perspectives · 2024Article
- Role of competitive endogenous RNA networks in the pathogenesis of coronary artery disease.Annals of translational medicine · 2021Article
- PRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms.Scientific reports · 2021Article
- SAlign-a structure aware method for global PPI network alignment.BMC bioinformatics · 2020Article
- Comprehensive Analysis of lncRNAs Related to the Prognosis of Esophageal Cancer Based on ceRNA Network and Cox Regression Model.BioMed research international · 2020Article
- Article
- Exploring Pharmacological Mechanisms of Xuefu Zhuyu Decoction in the Treatment of Traumatic Brain Injury via a Network Pharmacology Approach.Evidence-based complementary and alternative medicine : eCAM · 2018Article
- An integrated in silico approach for functional and structural impact of non- synonymous SNPs in the MYH1 gene in Jeju Native Pigs.BMC genetics · 2016Article
- Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information.FEBS open bio · 2015Article
- Network Topologies Decoding Cervical Cancer.PloS one · 2015Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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