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10.1093/bioinformatics/btab338 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btab338 33964132!8135978!33964132     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 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Bioinformatics 2021 ; 37 (19): 3337-3342 Nephropedia Template TP {{tp|p=33964132|t=2021. Alignment free sequence comparison methods and reservoir host prediction |pdf=|usr=}}{{33964132}} gab.com Text Alignment free sequence comparison methods and reservoir host prediction JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=33964132 #FOAvip MOTIVATION: The emergence and subsequent pandemic of the SARS-CoV-2 virus raised urgent questions about its origin and, particularly, its reservoir host. These types of questions are long-standing problems in the management of emerging infectious diseases and are linked to virus discovery programs and the prediction of viruses that are likely to become zoonotic. Conventional means to identify reservoir hosts have relied on surveillance, experimental studies and phylogenetics. More recently, machine learning approaches have been applied to generate tools to swiftly predict reservoir hosts from sequence data. RESULTS: Here, we extend a recent work that combined sequence alignment and a mixture of alignment-free approaches using a gradient boosting machines machine learning model, which integrates genomic traits and phylogenetic neighbourhood signatures to predict reservoir hosts. We add a more uniform approach by applying Machine Learning with Digital Signal Processing-based structural patterns. The extended model was applied to an existing virus/reservoir host dataset and to the SARS-CoV-2 and related viruses and generated an improvement in prediction accuracy. AVAILABILITY AND IMPLEMENTATION: The source code used in this work is freely available at https://github.com/bill1167/hostgbms. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Twit Text FOAVip Alignment free sequence comparison methods and reservoir host prediction ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=33964132 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33964132 #FOAvip English Wikipedia <ref>{{Cite pmid|33964132}}</ref> Alignment free sequence comparison methods and reservoir host prediction #MMPMID33964132 Lee B; Smith DK; Guan Y Bioinformatics 2021[Oct]; 37 (19): 3337-3342 PMID33964132show ga MOTIVATION: The emergence and subsequent pandemic of the SARS-CoV-2 virus raised urgent questions about its origin and, particularly, its reservoir host. These types of questions are long-standing problems in the management of emerging infectious diseases and are linked to virus discovery programs and the prediction of viruses that are likely to become zoonotic. Conventional means to identify reservoir hosts have relied on surveillance, experimental studies and phylogenetics. More recently, machine learning approaches have been applied to generate tools to swiftly predict reservoir hosts from sequence data. RESULTS: Here, we extend a recent work that combined sequence alignment and a mixture of alignment-free approaches using a gradient boosting machines machine learning model, which integrates genomic traits and phylogenetic neighbourhood signatures to predict reservoir hosts. We add a more uniform approach by applying Machine Learning with Digital Signal Processing-based structural patterns. The extended model was applied to an existing virus/reservoir host dataset and to the SARS-CoV-2 and related viruses and generated an improvement in prediction accuracy. AVAILABILITY AND IMPLEMENTATION: The source code used in this work is freely available at https://github.com/bill1167/hostgbms. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. äAlignment free sequence comparison methods and reservoir host predicti(epmc).pdf DeepDyve Pubget Overpricing