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10.1038/s41598-021-99399-2 http://scihub22266oqcxt.onion/10.1038/s41598-021-99399-2 34615934!8494853!34615934     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 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 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2021 ; 11 (1): 19839 Nephropedia Template TP {{tp|p=34615934|t=2021. Drug repositioning by merging active subnetworks validated in cancer and COVID-19 |pdf=|usr=}}{{34615934}} gab.com Text Drug repositioning by merging active subnetworks validated in cancer and COVID-19 JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34615934 #FOAvip Computational drug repositioning aims at ranking and selecting existing drugs for novel diseases or novel use in old diseases. In silico drug screening has the potential for speeding up considerably the shortlisting of promising candidates in response to outbreaks of diseases such as COVID-19 for which no satisfactory cure has yet been found. We describe DrugMerge as a methodology for preclinical computational drug repositioning based on merging multiple drug rankings obtained with an ensemble of disease active subnetworks. DrugMerge uses differential transcriptomic data on drugs and diseases in the context of a large gene co-expression network. Experiments with four benchmark diseases demonstrate that our method detects in first position drugs in clinical use for the specified disease, in all four cases. Application of DrugMerge to COVID-19 found rankings with many drugs currently in clinical trials for COVID-19 in top positions, thus showing that DrugMerge can mimic human expert judgment. Twit Text FOAVip Drug repositioning by merging active subnetworks validated in cancer and COVID-19 ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34615934 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34615934 #FOAvip English Wikipedia <ref>{{Cite pmid|34615934}}</ref> Drug repositioning by merging active subnetworks validated in cancer and COVID-19 #MMPMID34615934 Lucchetta M; Pellegrini M Sci Rep 2021[Oct]; 11 (1): 19839 PMID34615934show ga Computational drug repositioning aims at ranking and selecting existing drugs for novel diseases or novel use in old diseases. In silico drug screening has the potential for speeding up considerably the shortlisting of promising candidates in response to outbreaks of diseases such as COVID-19 for which no satisfactory cure has yet been found. We describe DrugMerge as a methodology for preclinical computational drug repositioning based on merging multiple drug rankings obtained with an ensemble of disease active subnetworks. DrugMerge uses differential transcriptomic data on drugs and diseases in the context of a large gene co-expression network. Experiments with four benchmark diseases demonstrate that our method detects in first position drugs in clinical use for the specified disease, in all four cases. Application of DrugMerge to COVID-19 found rankings with many drugs currently in clinical trials for COVID-19 in top positions, thus showing that DrugMerge can mimic human expert judgment. |*COVID-19 Drug Treatment[MESH] |Antineoplastic Agents/*pharmacology[MESH] |Antiviral Agents[MESH] |COVID-19/genetics/metabolism/virology[MESH] |Computational Biology/methods[MESH] |Databases, Genetic[MESH] |Databases, Pharmaceutical[MESH] |Drug Repositioning/*methods[MESH] |Gene Regulatory Networks[MESH] |Humans[MESH] |Neoplasms/*drug therapy/genetics/metabolism/virology[MESH]Drug repositioning by merging active subnetworks validated in cancer a(epmc).pdf DeepDyve Pubget Overpricing