Unsupervised identification of significant lineages of SARS-CoV-2 through scalable machine learning methods.

ABSTRACT: Since its emergence in late 2019, SARS-CoV-2 has diversified into a large number of lineages and caused multiple waves of infection globally. Novel lineages have the potential to spread rapidly and internationally if they have higher intrinsic transmissibility and/or can evade host immune responses, as has been seen with the Alpha, Delta, and Omicron variants of concern. They can also cause increased mortality and morbidity if they have increased virulence, as was seen for Alpha and Delta. Phylogenetic methods provide the "gold standard" for representing the global diversity of SARS-CoV-2 and to identify newly emerging lineages. However, these methods are computationally expensive, struggle when datasets get too large, and require manual curation to designate new lineages. These challenges

SUBMITTER: Cahuantzi R 

PROVIDER: S-EPMC10962941 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature