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10.1371/journal.pcbi.1005085 http://scihub22266oqcxt.onion/10.1371/journal.pcbi.1005085 C5001716!5001716!27563720     free     free     free Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+Comput+Biol 2016 ; 12 (8): ä Nephropedia Template TP {{tp|p=27563720|t=2016. Efficient Reconstruction of Predictive Consensus Metabolic Network Models |pdf=|usr=}}{{27563720}} gab.com Text Efficient Reconstruction of Predictive Consensus Metabolic Network Models JO: PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=27563720 #FOAvip Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions. Twit Text FOAVip Efficient Reconstruction of Predictive Consensus Metabolic Network Models ????PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=27563720 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27563720 #FOAvip English Wikipedia <ref>{{Cite pmid|27563720}}</ref> Efficient Reconstruction of Predictive Consensus Metabolic Network Models #MMPMID27563720 van Heck RGA; Ganter M; Martins dos Santos VAP; Stelling J PLoS Comput Biol 2016[Aug]; 12 (8): ä PMID27563720show ga Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions. äEfficient Reconstruction of Predictive Consensus Metabolic Network Mod(epmc).pdf DeepDyve Pubget Overpricing