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10.1186/1755-8794-8-S2-S2 http://scihub22266oqcxt.onion/10.1186/1755-8794-8-S2-S2 C4460611!4460611!26044949     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Med+Genomics 2015 ; 8 (Suppl 2): S2 Nephropedia Template TP {{tp|p=26044949|t=2015. Inferring drug-disease associations based on known protein complexes |pdf=|usr=}}{{26044949}} gab.com Text Inferring drug-disease associations based on known protein complexes JO: BMC Med Genomics http://www.kidney.de/pget.php?&tw=1&pmid=26044949 #FOAvip Inferring drug-disease associations is critical in unveiling disease mechanisms, as well as discovering novel functions of available drugs, or drug repositioning. Previous work is primarily based on drug-gene-disease relationship, which throws away many important information since genes execute their functions through interacting others. To overcome this issue, we propose a novel methodology that discover the drug-disease association based on protein complexes. Firstly, the integrated heterogeneous network consisting of drugs, protein complexes, and disease are constructed, where we assign weights to the drug-disease association by using probability. Then, from the tripartite network, we get the indirect weighted relationships between drugs and diseases. The larger the weight, the higher the reliability of the correlation. We apply our method to mental disorders and hypertension, and validate the result by using comparative toxicogenomics database. Our ranked results can be directly reinforced by existing biomedical literature, suggesting that our proposed method obtains higher specificity and sensitivity. The proposed method offers new insight into drug-disease discovery. Our method is publicly available at http://1.complexdrug.sinaapp.com/Drug_Complex_Disease/Data_Download.html. Twit Text FOAVip Inferring drug-disease associations based on known protein complexes ????BMC Med Genomics http://www.kidney.de/pget.php?&tw=1&pmid=26044949 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26044949 #FOAvip English Wikipedia <ref>{{Cite pmid|26044949}}</ref> Inferring drug-disease associations based on known protein complexes #MMPMID26044949 Yu L; Huang J; Ma Z; Zhang J; Zou Y; Gao L BMC Med Genomics 2015[]; 8 (Suppl 2): S2 PMID26044949show ga Inferring drug-disease associations is critical in unveiling disease mechanisms, as well as discovering novel functions of available drugs, or drug repositioning. Previous work is primarily based on drug-gene-disease relationship, which throws away many important information since genes execute their functions through interacting others. To overcome this issue, we propose a novel methodology that discover the drug-disease association based on protein complexes. Firstly, the integrated heterogeneous network consisting of drugs, protein complexes, and disease are constructed, where we assign weights to the drug-disease association by using probability. Then, from the tripartite network, we get the indirect weighted relationships between drugs and diseases. The larger the weight, the higher the reliability of the correlation. We apply our method to mental disorders and hypertension, and validate the result by using comparative toxicogenomics database. Our ranked results can be directly reinforced by existing biomedical literature, suggesting that our proposed method obtains higher specificity and sensitivity. The proposed method offers new insight into drug-disease discovery. Our method is publicly available at http://1.complexdrug.sinaapp.com/Drug_Complex_Disease/Data_Download.html. äInferring drug-disease associations based on known protein complexes (epmc).pdf DeepDyve Pubget Overpricing