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10.3389/fphar.2018.00697 http://scihub22266oqcxt.onion/10.3389/fphar.2018.00697 C6043797!6043797!30034338     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       Front+Pharmacol 2018 ; 9 (ä): ä Nephropedia Template TP {{tp|p=30034338|t=2018. Master Regulators Connectivity Map: A Transcription Factors-Centered Approach to Drug Repositioning |pdf=|usr=}}{{30034338}} gab.com Text Master Regulators Connectivity Map: A Transcription Factors-Centered Approach to Drug Repositioning JO: Front Pharmacol http://www.kidney.de/pget.php?&tw=1&pmid=30034338 #FOAvip Drug discovery is a very expensive and time-consuming endeavor. Fortunately, recent omics technologies and Systems Biology approaches introduced interesting new tools to achieve this task, facilitating the repurposing of already known drugs to new therapeutic assignments using gene expression data and bioinformatics. The inherent role of transcription factors in gene expression modulation makes them strong candidates for master regulators of phenotypic transitions. However, transcription factors expression itself usually does not reflect its activity changes due to post-transcriptional modifications and other complications. In this aspect, the use of high-throughput transcriptomic data may be employed to infer transcription factors-targets interactions and assess their activity through co-expression networks, which can be further used to search for drugs capable of reverting the gene expression profile of pathological phenotypes employing the connectivity maps paradigm. Following this idea, we argue that a module-oriented connectivity map approach using transcription factors-centered networks would aid the query for new repositioning candidates. Through a brief case study, we explored this idea in bipolar disorder, retrieving known drugs used in the usual clinical scenario as well as new candidates with potential therapeutic application in this disease. Indeed, the results of the case study indicate just how promising our approach may be to drug repositioning. Twit Text FOAVip Master Regulators Connectivity Map: A Transcription Factors-Centered Approach to Drug Repositioning ????Front Pharmacol http://www.kidney.de/pget.php?&tw=1&pmid=30034338 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=30034338 #FOAvip English Wikipedia <ref>{{Cite pmid|30034338}}</ref> Master Regulators Connectivity Map: A Transcription Factors-Centered Approach to Drug Repositioning #MMPMID30034338 De Bastiani MA; Pfaffenseller B; Klamt F Front Pharmacol 2018[]; 9 (ä): ä PMID30034338show ga Drug discovery is a very expensive and time-consuming endeavor. Fortunately, recent omics technologies and Systems Biology approaches introduced interesting new tools to achieve this task, facilitating the repurposing of already known drugs to new therapeutic assignments using gene expression data and bioinformatics. The inherent role of transcription factors in gene expression modulation makes them strong candidates for master regulators of phenotypic transitions. However, transcription factors expression itself usually does not reflect its activity changes due to post-transcriptional modifications and other complications. In this aspect, the use of high-throughput transcriptomic data may be employed to infer transcription factors-targets interactions and assess their activity through co-expression networks, which can be further used to search for drugs capable of reverting the gene expression profile of pathological phenotypes employing the connectivity maps paradigm. Following this idea, we argue that a module-oriented connectivity map approach using transcription factors-centered networks would aid the query for new repositioning candidates. Through a brief case study, we explored this idea in bipolar disorder, retrieving known drugs used in the usual clinical scenario as well as new candidates with potential therapeutic application in this disease. Indeed, the results of the case study indicate just how promising our approach may be to drug repositioning. äMaster Regulators Connectivity Map: A Transcription Factors-Centered A(epmc).pdf DeepDyve Pubget Overpricing