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10.1038/s41540-017-0007-2 http://scihub22266oqcxt.onion/10.1038/s41540-017-0007-2 C5445589!5445589 !28649433     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28649433 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       NPJ+Syst+Biol+Appl 2017 ; 3 (ä): 6 Nephropedia Template TP {{tp|p=28649433 |t=2017. On the performance of de novo pathway enrichment |pdf=|usr=}}{{28649433 }} gab.com Text On the performance of de novo pathway enrichment JO: NPJ Syst Biol Appl http://www.kidney.de/pget.php?&tw=1&pmid=28649433 #FOAvip De novo pathway enrichment is a powerful approach to discover previously uncharacterized molecular mechanisms in addition to already known pathways. To achieve this, condition-specific functional modules are extracted from large interaction networks. Here, we give an overview of the state of the art and present the first framework for assessing the performance of existing methods. We identified 19 tools and selected seven representative candidates for a comparative analysis with more than 12,000 runs, spanning different biological networks, molecular profiles, and parameters. Our results show that none of the methods consistently outperforms the others. To mitigate this issue for biomedical researchers, we provide guidelines to choose the appropriate tool for a given dataset. Moreover, our framework is the first attempt for a quantitative evaluation of de novo methods, which will allow the bioinformatics community to objectively compare future tools against the state of the art. Twit Text FOAVip On the performance of de novo pathway enrichment ????NPJ Syst Biol Appl http://www.kidney.de/pget.php?&tw=1&pmid=28649433 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28649433 #FOAvip English Wikipedia <ref>{{Cite pmid|28649433 }}</ref> On the performance of de novo pathway enrichment #MMPMID28649433 Batra R ; Alcaraz N ; Gitzhofer K ; Pauling J ; Ditzel HJ ; Hellmuth M ; Baumbach J ; List M NPJ Syst Biol Appl 2017[]; 3 (ä): 6 PMID28649433 show ga De novo pathway enrichment is a powerful approach to discover previously uncharacterized molecular mechanisms in addition to already known pathways. To achieve this, condition-specific functional modules are extracted from large interaction networks. Here, we give an overview of the state of the art and present the first framework for assessing the performance of existing methods. We identified 19 tools and selected seven representative candidates for a comparative analysis with more than 12,000 runs, spanning different biological networks, molecular profiles, and parameters. Our results show that none of the methods consistently outperforms the others. To mitigate this issue for biomedical researchers, we provide guidelines to choose the appropriate tool for a given dataset. Moreover, our framework is the first attempt for a quantitative evaluation of de novo methods, which will allow the bioinformatics community to objectively compare future tools against the state of the art. äOn the performance of de novo pathway enrichment (epmc).pdf DeepDyve Pubget Overpricing