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10.1007/s12559-020-09790-w http://scihub22266oqcxt.onion/10.1007/s12559-020-09790-w 33456620!7804214!33456620     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Cognit+Comput 2021 ; ä (ä): 1-12 Nephropedia Template TP {{tp|p=33456620|t=2021. Principal Component Analysis Applications in COVID-19 Genome Sequence Studies |pdf=|usr=}}{{33456620}} gab.com Text Principal Component Analysis Applications in COVID-19 Genome Sequence Studies JO: Cognit Comput http://www.kidney.de/pget.php?&tw=1&pmid=33456620 #FOAvip RNA genomes from coronavirus have a length as long as 32 kilobases, and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the outbreak of coronavirus disease 2019 (COVID-19) pandemic has long sequences which made the analysis difficult. Over 20,000 sequences have been submitted to GISAID, and the number is growing fast each day which increased the difficulties in data analysis; however, genome sequence analysis is critical in understanding the COVID-19 and preventing the spread of the disease. In this study, a principal component analysis (PCA) was applied to the aligned large size genome sequences and the numerical numbers were converted from the letters using a published method designed for protein sequence cluster analysis. The study initialized with a shortlist sequence testing, and the PCA score plot showed high tolerance with low-quality data, and the major virus sequences from humans were separated from the pangolin and bat samples. Our study also successfully built a model for a large number of sequences with more than 20,000 sequences which indicate the potential mutation directions for the COVID-19 which can be served as a pretreatment method for detailed studies such as decision tree-based methods. In summary, our study provided a fast tool to analyze the high-volume genome sequences such as the COVID-19 and successfully applied to more than 20,000 sequences which may provide mutation direction information for COVID-19 studies. Twit Text FOAVip Principal Component Analysis Applications in COVID-19 Genome Sequence Studies ????Cognit Comput http://www.kidney.de/pget.php?&tw=1&pmid=33456620 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33456620 #FOAvip English Wikipedia <ref>{{Cite pmid|33456620}}</ref> Principal Component Analysis Applications in COVID-19 Genome Sequence Studies #MMPMID33456620 Wang B; Jiang L Cognit Comput 2021[Jan]; ä (ä): 1-12 PMID33456620show ga RNA genomes from coronavirus have a length as long as 32 kilobases, and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the outbreak of coronavirus disease 2019 (COVID-19) pandemic has long sequences which made the analysis difficult. Over 20,000 sequences have been submitted to GISAID, and the number is growing fast each day which increased the difficulties in data analysis; however, genome sequence analysis is critical in understanding the COVID-19 and preventing the spread of the disease. In this study, a principal component analysis (PCA) was applied to the aligned large size genome sequences and the numerical numbers were converted from the letters using a published method designed for protein sequence cluster analysis. The study initialized with a shortlist sequence testing, and the PCA score plot showed high tolerance with low-quality data, and the major virus sequences from humans were separated from the pangolin and bat samples. Our study also successfully built a model for a large number of sequences with more than 20,000 sequences which indicate the potential mutation directions for the COVID-19 which can be served as a pretreatment method for detailed studies such as decision tree-based methods. In summary, our study provided a fast tool to analyze the high-volume genome sequences such as the COVID-19 and successfully applied to more than 20,000 sequences which may provide mutation direction information for COVID-19 studies. äPrincipal Component Analysis Applications in COVID-19 Genome Sequence (epmc).pdf DeepDyve Pubget Overpricing