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Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks
Background. The molecular profiles exhibited in different cancer types are very different; hence, discovering distinct functional modules associated with specific cancer types is very important to understand the distinct functions associated with them. Protein-protein interaction networks carry vita...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4606133/ https://www.ncbi.nlm.nih.gov/pubmed/26495282 http://dx.doi.org/10.1155/2015/146365 |
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author | Shen, Ru Wang, Xiaosheng Guda, Chittibabu |
author_facet | Shen, Ru Wang, Xiaosheng Guda, Chittibabu |
author_sort | Shen, Ru |
collection | PubMed |
description | Background. The molecular profiles exhibited in different cancer types are very different; hence, discovering distinct functional modules associated with specific cancer types is very important to understand the distinct functions associated with them. Protein-protein interaction networks carry vital information about molecular interactions in cellular systems, and identification of functional modules (subgraphs) in these networks is one of the most important applications of biological network analysis. Results. In this study, we developed a new graph theory based method to identify distinct functional modules from nine different cancer protein-protein interaction networks. The method is composed of three major steps: (i) extracting modules from protein-protein interaction networks using network clustering algorithms; (ii) identifying distinct subgraphs from the derived modules; and (iii) identifying distinct subgraph patterns from distinct subgraphs. The subgraph patterns were evaluated using experimentally determined cancer-specific protein-protein interaction data from the Ingenuity knowledgebase, to identify distinct functional modules that are specific to each cancer type. Conclusion. We identified cancer-type specific subgraph patterns that may represent the functional modules involved in the molecular pathogenesis of different cancer types. Our method can serve as an effective tool to discover cancer-type specific functional modules from large protein-protein interaction networks. |
format | Online Article Text |
id | pubmed-4606133 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-46061332015-10-22 Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks Shen, Ru Wang, Xiaosheng Guda, Chittibabu Biomed Res Int Research Article Background. The molecular profiles exhibited in different cancer types are very different; hence, discovering distinct functional modules associated with specific cancer types is very important to understand the distinct functions associated with them. Protein-protein interaction networks carry vital information about molecular interactions in cellular systems, and identification of functional modules (subgraphs) in these networks is one of the most important applications of biological network analysis. Results. In this study, we developed a new graph theory based method to identify distinct functional modules from nine different cancer protein-protein interaction networks. The method is composed of three major steps: (i) extracting modules from protein-protein interaction networks using network clustering algorithms; (ii) identifying distinct subgraphs from the derived modules; and (iii) identifying distinct subgraph patterns from distinct subgraphs. The subgraph patterns were evaluated using experimentally determined cancer-specific protein-protein interaction data from the Ingenuity knowledgebase, to identify distinct functional modules that are specific to each cancer type. Conclusion. We identified cancer-type specific subgraph patterns that may represent the functional modules involved in the molecular pathogenesis of different cancer types. Our method can serve as an effective tool to discover cancer-type specific functional modules from large protein-protein interaction networks. Hindawi Publishing Corporation 2015 2015-09-30 /pmc/articles/PMC4606133/ /pubmed/26495282 http://dx.doi.org/10.1155/2015/146365 Text en Copyright © 2015 Ru Shen et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Shen, Ru Wang, Xiaosheng Guda, Chittibabu Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks |
title | Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks |
title_full | Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks |
title_fullStr | Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks |
title_full_unstemmed | Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks |
title_short | Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks |
title_sort | discovering distinct functional modules of specific cancer types using protein-protein interaction networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4606133/ https://www.ncbi.nlm.nih.gov/pubmed/26495282 http://dx.doi.org/10.1155/2015/146365 |
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