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10.1016/j.bbrc.2020.09.010 http://scihub22266oqcxt.onion/10.1016/j.bbrc.2020.09.010 32981683!7500881!32981683     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 269.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biochem+Biophys+Res+Commun 2020 ; 533 (3): 553-558 Nephropedia Template TP {{tp|p=32981683|t=2020. Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone |pdf=|usr=}}{{32981683}} gab.com Text Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone JO: Biochem Biophys Res Commun http://www.kidney.de/pget.php?&tw=1&pmid=32981683 #FOAvip Coronaviruses infect many animals, including humans, due to interspecies transmission. Three of the known human coronaviruses: MERS, SARS-CoV-1, and SARS-CoV-2, the pathogen for the COVID-19 pandemic, cause severe disease. Improved methods to predict host specificity of coronaviruses will be valuable for identifying and controlling future outbreaks. The coronavirus S protein plays a key role in host specificity by attaching the virus to receptors on the cell membrane. We analyzed 1238 spike sequences for their host specificity. Spike sequences readily segregate in t-SNE embeddings into clusters of similar hosts and/or virus species. Machine learning with SVM, Logistic Regression, Decision Tree, Random Forest gave high average accuracies, F(1) scores, sensitivities and specificities of 0.95-0.99. Importantly, sites identified by Decision Tree correspond to protein regions with known biological importance. These results demonstrate that spike sequences alone can be used to predict host specificity. Twit Text FOAVip Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone ????Biochem Biophys Res Commun http://www.kidney.de/pget.php?&tw=1&pmid=32981683 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32981683 #FOAvip English Wikipedia <ref>{{Cite pmid|32981683}}</ref> Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone #MMPMID32981683 Kuzmin K; Adeniyi AE; DaSouza AK Jr; Lim D; Nguyen H; Molina NR; Xiong L; Weber IT; Harrison RW Biochem Biophys Res Commun 2020[Dec]; 533 (3): 553-558 PMID32981683show ga Coronaviruses infect many animals, including humans, due to interspecies transmission. Three of the known human coronaviruses: MERS, SARS-CoV-1, and SARS-CoV-2, the pathogen for the COVID-19 pandemic, cause severe disease. Improved methods to predict host specificity of coronaviruses will be valuable for identifying and controlling future outbreaks. The coronavirus S protein plays a key role in host specificity by attaching the virus to receptors on the cell membrane. We analyzed 1238 spike sequences for their host specificity. Spike sequences readily segregate in t-SNE embeddings into clusters of similar hosts and/or virus species. Machine learning with SVM, Logistic Regression, Decision Tree, Random Forest gave high average accuracies, F(1) scores, sensitivities and specificities of 0.95-0.99. Importantly, sites identified by Decision Tree correspond to protein regions with known biological importance. These results demonstrate that spike sequences alone can be used to predict host specificity. |*Host Specificity[MESH] |*Machine Learning[MESH] |*Spike Glycoprotein, Coronavirus/chemistry[MESH] |Animals[MESH] |Computational Biology/*methods[MESH] |Coronavirus/*pathogenicity[MESH]Machine learning methods accurately predict host specificity of corona(epmc).pdf DeepDyve Pubget Overpricing