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10.1155/2015/146365 http://scihub22266oqcxt.onion/10.1155/2015/146365 C4606133!4606133!26495282     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26495282&cmd=llinks): Failed to open stream: HTTP request failed! 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Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks |pdf=|usr=}}{{26495282}} gab.com Text Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=26495282 #FOAvip 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. Twit Text FOAVip Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=26495282 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26495282 #FOAvip English Wikipedia <ref>{{Cite pmid|26495282}}</ref> Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks #MMPMID26495282 Shen R; Wang X; Guda C Biomed Res Int 2015[]; 2015 (ä): ä PMID26495282show ga 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. äDiscovering Distinct Functional Modules of Specific Cancer Types Using(epmc).pdf DeepDyve Pubget Overpricing