Can machines learn the mutation signatures of SARS-CoV-2 and enable viral-genotype guided predictive prognosis?

ABSTRACT:

Motivation

Continuous emergence of new variants through appearance/accumulation/disappearance of mutations is a hallmark of many viral diseases. SARS-CoV-2 variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of variants and huge scale of genomic data have added to the challenges of tracing the mutations/variants and their relationship to infection severity (if any).

Results

We explored the suitability of virus-genotype guided machine-learning in infection prognosis and identification of features/mutations-of-interest. Total 199,519 outcome-traced genomes, representing 45,625 nucleotide-mutations, were employed. Among these, post data-cleaning, Low and High severi

SUBMITTER: Nagpal S 

PROVIDER: S-EPMC9188262 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature