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10.1186/1752-0509-7-S5-S6 http://scihub22266oqcxt.onion/10.1186/1752-0509-7-S5-S6 C4029299!4029299 !24564976     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\24564976 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       BMC+Syst+Biol 2013 ; 7 Suppl 5 (Suppl 5 ): S6 Nephropedia Template TP {{tp|p=24564976 |t=2013. Computational drug repositioning through heterogeneous network clustering |pdf=|usr=}}{{24564976 }} gab.com Text Computational drug repositioning through heterogeneous network clustering JO: BMC Syst Biol http://www.kidney.de/pget.php?&tw=1&pmid=24564976 #FOAvip BACKGROUND: Given the costly and time consuming process and high attrition rates in drug discovery and development, drug repositioning or drug repurposing is considered as a viable strategy both to replenish the drying out drug pipelines and to surmount the innovation gap. Although there is a growing recognition that mechanistic relationships from molecular to systems level should be integrated into drug discovery paradigms, relatively few studies have integrated information about heterogeneous networks into computational drug-repositioning candidate discovery platforms. RESULTS: Using known disease-gene and drug-target relationships from the KEGG database, we built a weighted disease and drug heterogeneous network. The nodes represent drugs or diseases while the edges represent shared gene, biological process, pathway, phenotype or a combination of these features. We clustered this weighted network to identify modules and then assembled all possible drug-disease pairs (putative drug repositioning candidates) from these modules. We validated our predictions by testing their robustness and evaluated them by their overlap with drug indications that were either reported in published literature or investigated in clinical trials. CONCLUSIONS: Previous computational approaches for drug repositioning focused either on drug-drug and disease-disease similarity approaches whereas we have taken a more holistic approach by considering drug-disease relationships also. Further, we considered not only gene but also other features to build the disease drug networks. Despite the relative simplicity of our approach, based on the robustness analyses and the overlap of some of our predictions with drug indications that are under investigation, we believe our approach could complement the current computational approaches for drug repositioning candidate discovery. Twit Text FOAVip Computational drug repositioning through heterogeneous network clustering ????BMC Syst Biol http://www.kidney.de/pget.php?&tw=1&pmid=24564976 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24564976 #FOAvip English Wikipedia <ref>{{Cite pmid|24564976 }}</ref> Computational drug repositioning through heterogeneous network clustering #MMPMID24564976 Wu C ; Gudivada RC ; Aronow BJ ; Jegga AG BMC Syst Biol 2013[]; 7 Suppl 5 (Suppl 5 ): S6 PMID24564976 show ga BACKGROUND: Given the costly and time consuming process and high attrition rates in drug discovery and development, drug repositioning or drug repurposing is considered as a viable strategy both to replenish the drying out drug pipelines and to surmount the innovation gap. Although there is a growing recognition that mechanistic relationships from molecular to systems level should be integrated into drug discovery paradigms, relatively few studies have integrated information about heterogeneous networks into computational drug-repositioning candidate discovery platforms. RESULTS: Using known disease-gene and drug-target relationships from the KEGG database, we built a weighted disease and drug heterogeneous network. The nodes represent drugs or diseases while the edges represent shared gene, biological process, pathway, phenotype or a combination of these features. We clustered this weighted network to identify modules and then assembled all possible drug-disease pairs (putative drug repositioning candidates) from these modules. We validated our predictions by testing their robustness and evaluated them by their overlap with drug indications that were either reported in published literature or investigated in clinical trials. CONCLUSIONS: Previous computational approaches for drug repositioning focused either on drug-drug and disease-disease similarity approaches whereas we have taken a more holistic approach by considering drug-disease relationships also. Further, we considered not only gene but also other features to build the disease drug networks. Despite the relative simplicity of our approach, based on the robustness analyses and the overlap of some of our predictions with drug indications that are under investigation, we believe our approach could complement the current computational approaches for drug repositioning candidate discovery. |Alzheimer Disease/drug therapy [MESH] |Amyloid Precursor Protein Secretases/antagonists & inhibitors [MESH] |Anilides/therapeutic use [MESH] |Basal Cell Nevus Syndrome/drug therapy/genetics [MESH] |Cluster Analysis [MESH] |Computational Biology/*methods [MESH] |Computer Graphics [MESH] |Databases, Factual [MESH] |Drug Repositioning/*methods [MESH] |Hidradenitis Suppurativa/drug therapy [MESH] |Humans [MESH] |Phenotype [MESH] |Protease Inhibitors/pharmacology/therapeutic use [MESH]Computational drug repositioning through heterogeneous network cluster(epmc).pdf DeepDyve Pubget Overpricing