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Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network
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 d...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Ivyspring International Publisher
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4081603/ https://www.ncbi.nlm.nih.gov/pubmed/25013377 http://dx.doi.org/10.7150/ijbs.8430 |
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author | Yang, Lei Zhao, Xudong Tang, Xianglong |
author_facet | Yang, Lei Zhao, Xudong Tang, Xianglong |
author_sort | Yang, Lei |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4081603 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Ivyspring International Publisher |
record_format | MEDLINE/PubMed |
spelling | pubmed-40816032014-07-10 Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network Yang, Lei Zhao, Xudong Tang, Xianglong Int J Biol Sci Research Paper 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. Ivyspring International Publisher 2014-06-11 /pmc/articles/PMC4081603/ /pubmed/25013377 http://dx.doi.org/10.7150/ijbs.8430 Text en © Ivyspring International Publisher. This is an open-access article distributed under the terms of the Creative Commons License (http://creativecommons.org/licenses/by-nc-nd/3.0/). Reproduction is permitted for personal, noncommercial use, provided that the article is in whole, unmodified, and properly cited. |
spellingShingle | Research Paper Yang, Lei Zhao, Xudong Tang, Xianglong Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network |
title | Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network |
title_full | Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network |
title_fullStr | Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network |
title_full_unstemmed | Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network |
title_short | Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network |
title_sort | predicting disease-related proteins based on clique backbone in protein-protein interaction network |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4081603/ https://www.ncbi.nlm.nih.gov/pubmed/25013377 http://dx.doi.org/10.7150/ijbs.8430 |
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