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10.1038/s41598-018-28330-z http://scihub22266oqcxt.onion/10.1038/s41598-018-28330-z C6033909!6033909!29977071     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2018 ; 8 (ä): ä Nephropedia Template TP {{tp|p=29977071|t=2018. Using machine learning tools for protein database biocuration assistance |pdf=|usr=}}{{29977071}} gab.com Text Using machine learning tools for protein database biocuration assistance JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=29977071 #FOAvip Biocuration in the omics sciences has become paramount, as research in these fields rapidly evolves towards increasingly data-dependent models. As a result, the management of web-accessible publicly-available databases becomes a central task in biological knowledge dissemination. One relevant challenge for biocurators is the unambiguous identification of biological entities. In this study, we illustrate the adequacy of machine learning methods as biocuration assistance tools using a publicly available protein database as an example. This database contains information on G Protein-Coupled Receptors (GPCRs), which are part of eukaryotic cell membranes and relevant in cell communication as well as major drug targets in pharmacology. These receptors are characterized according to subtype labels. Previous analysis of this database provided evidence that some of the receptor sequences could be affected by a case of label noise, as they appeared to be too consistently misclassified by machine learning methods. Here, we extend our analysis to recent and quite substantially modified new versions of the database and reveal their now extremely accurate labeling using several machine learning models and different transformations of the unaligned sequences. These findings support the adequacy of our proposed method to identify problematic labeling cases as a tool for database biocuration. Twit Text FOAVip Using machine learning tools for protein database biocuration assistance ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=29977071 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29977071 #FOAvip English Wikipedia <ref>{{Cite pmid|29977071}}</ref> Using machine learning tools for protein database biocuration assistance #MMPMID29977071 König C; Shaim I; Vellido A; Romero E; Alquézar R; Giraldo J Sci Rep 2018[]; 8 (ä): ä PMID29977071show ga Biocuration in the omics sciences has become paramount, as research in these fields rapidly evolves towards increasingly data-dependent models. As a result, the management of web-accessible publicly-available databases becomes a central task in biological knowledge dissemination. One relevant challenge for biocurators is the unambiguous identification of biological entities. In this study, we illustrate the adequacy of machine learning methods as biocuration assistance tools using a publicly available protein database as an example. This database contains information on G Protein-Coupled Receptors (GPCRs), which are part of eukaryotic cell membranes and relevant in cell communication as well as major drug targets in pharmacology. These receptors are characterized according to subtype labels. Previous analysis of this database provided evidence that some of the receptor sequences could be affected by a case of label noise, as they appeared to be too consistently misclassified by machine learning methods. Here, we extend our analysis to recent and quite substantially modified new versions of the database and reveal their now extremely accurate labeling using several machine learning models and different transformations of the unaligned sequences. These findings support the adequacy of our proposed method to identify problematic labeling cases as a tool for database biocuration. äUsing machine learning tools for protein database biocuration assistan(epmc).pdf DeepDyve Pubget Overpricing