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10.1038/s41598-021-93757-w http://scihub22266oqcxt.onion/10.1038/s41598-021-93757-w 34267263!8282786!34267263     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2021 ; 11 (1): 14558 Nephropedia Template TP {{tp|p=34267263|t=2021. A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction |pdf=|usr=}}{{34267263}} gab.com Text A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34267263 #FOAvip Whereas accelerated attention beclouded early stages of the coronavirus spread, knowledge of actual pathogenicity and origin of possible sub-strains remained unclear. By harvesting the Global initiative on Sharing All Influenza Data (GISAID) database ( https://www.gisaid.org/ ), between December 2019 and January 15, 2021, a total of 8864 human SARS-CoV-2 complete genome sequences processed by gender, across 6 continents (88 countries) of the world, Antarctica exempt, were analyzed. We hypothesized that data speak for itself and can discern true and explainable patterns of the disease. Identical genome diversity and pattern correlates analysis performed using a hybrid of biotechnology and machine learning methods corroborate the emergence of inter- and intra- SARS-CoV-2 sub-strains transmission and sustain an increase in sub-strains within the various continents, with nucleotide mutations dynamically varying between individuals in close association with the virus as it adapts to its host/environment. Interestingly, some viral sub-strain patterns progressively transformed into new sub-strain clusters indicating varying amino acid, and strong nucleotide association derived from same lineage. A novel cognitive approach to knowledge mining helped the discovery of transmission routes and seamless contact tracing protocol. Our classification results were better than state-of-the-art methods, indicating a more robust system for predicting emerging or new viral sub-strain(s). The results therefore offer explanations for the growing concerns about the virus and its next wave(s). A future direction of this work is a defuzzification of confusable pattern clusters for precise intra-country SARS-CoV-2 sub-strains analytics. Twit Text FOAVip A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34267263 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34267263 #FOAvip English Wikipedia <ref>{{Cite pmid|34267263}}</ref> A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction #MMPMID34267263 Ekpenyong ME; Edoho ME; Inyang UG; Uzoka FM; Ekaidem IS; Moses AE; Emeje MO; Tatfeng YM; Udo IJ; Anwana ED; Etim OE; Geoffery JI; Dan EA Sci Rep 2021[Jul]; 11 (1): 14558 PMID34267263show ga Whereas accelerated attention beclouded early stages of the coronavirus spread, knowledge of actual pathogenicity and origin of possible sub-strains remained unclear. By harvesting the Global initiative on Sharing All Influenza Data (GISAID) database ( https://www.gisaid.org/ ), between December 2019 and January 15, 2021, a total of 8864 human SARS-CoV-2 complete genome sequences processed by gender, across 6 continents (88 countries) of the world, Antarctica exempt, were analyzed. We hypothesized that data speak for itself and can discern true and explainable patterns of the disease. Identical genome diversity and pattern correlates analysis performed using a hybrid of biotechnology and machine learning methods corroborate the emergence of inter- and intra- SARS-CoV-2 sub-strains transmission and sustain an increase in sub-strains within the various continents, with nucleotide mutations dynamically varying between individuals in close association with the virus as it adapts to its host/environment. Interestingly, some viral sub-strain patterns progressively transformed into new sub-strain clusters indicating varying amino acid, and strong nucleotide association derived from same lineage. A novel cognitive approach to knowledge mining helped the discovery of transmission routes and seamless contact tracing protocol. Our classification results were better than state-of-the-art methods, indicating a more robust system for predicting emerging or new viral sub-strain(s). The results therefore offer explanations for the growing concerns about the virus and its next wave(s). A future direction of this work is a defuzzification of confusable pattern clusters for precise intra-country SARS-CoV-2 sub-strains analytics. |COVID-19/epidemiology/transmission/*virology[MESH] |Computational Biology/methods[MESH] |DNA, Viral/genetics[MESH] |Databases, Genetic[MESH] |Forecasting/methods[MESH] |Genome, Viral[MESH] |Humans[MESH] |Machine Learning[MESH] |Mutation[MESH] |Phylogeny[MESH] |SARS-CoV-2/classification/*genetics/pathogenicity[MESH] |Sequence Analysis, DNA/*methods[MESH]A hybrid computational framework for intelligent inter-continent SARS-(epmc).pdf DeepDyve Pubget Overpricing