Machine learning-based approach KEVOLVE efficiently identifies SARS-CoV-2 variant-specific genomic signatures.

ABSTRACT: Machine learning was shown to be effective at identifying distinctive genomic signatures among viral sequences. These signatures are defined as pervasive motifs in the viral genome that allow discrimination between species or variants. In the context of SARS-CoV-2, the identification of these signatures can assist in taxonomic and phylogenetic studies, improve in the recognition and definition of emerging variants, and aid in the characterization of functional properties of polymorphic gene products. In this paper, we assess KEVOLVE, an approach based on a genetic algorithm with a machine-learning kernel, to identify multiple genomic signatures based on minimal sets of k-mers. In a comparative study, in which we analyzed large SARS-CoV-2 genome dataset, KEVOLVE was more effective at identi

SUBMITTER: Lebatteux D 

PROVIDER: S-EPMC10798494 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature