Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences.

ABSTRACT: We present an approach to discriminate SARS-CoV-2 virus types based on their RNA sequence descriptions avoiding a sequence alignment. For that purpose, sequences are preprocessed by feature extraction and the resulting feature vectors are analyzed by prototype-based classification to remain interpretable. In particular, we propose to use variants of learning vector quantization (LVQ) based on dissimilarity measures for RNA sequence data. The respective matrix LVQ provides additional knowledge about the classification decisions like discriminant feature correlations and, additionally, can be equipped with easy to realize reject options for uncertain data. Those options provide self-controlled evidence, i.e., the model refuses to make a classification decision if the model evidence for the p

SUBMITTER: Kaden M 

PROVIDER: S-EPMC8076884 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature