Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequencesS.i. : Wsom 2019 Published on 2021-04-272022-10-29 Journal: Neural Computing & Applications [Category] COVID-19, [키워드] Analysis analyzed approach Atypical Classifier Clustering coronavirus correlation dataset description dissimilarity Evidence genomic sequence analysis GISAID IMPROVE Interpretable models knowledge Last learning Learning vector quantization measure molecular Mutation new virus nucleotide Phylogenetic tree Population provide Reject options rejection resulting RNA sequence SARS-CoV-2 SARS-CoV-2 virus sequence supplementary material These data trained model variant vector viral sequences virus [DOI] 10.1007/s00521-021-06018-2 PMC 바로가기 [Article Type] S.i. : Wsom 2019