Identifying COVID-19 Severity-Related SARS-CoV-2 Mutation Using a Machine Learning Method.

ABSTRACT: SARS-CoV-2 shows great evolutionary capacity through a high frequency of genomic variation during transmission. Evolved SARS-CoV-2 often demonstrates resistance to previous vaccines and can cause poor clinical status in patients. Mutations in the SARS-CoV-2 genome involve mutations in structural and nonstructural proteins, and some of these proteins such as spike proteins have been shown to be directly associated with the clinical status of patients with severe COVID-19 pneumonia. In this study, we collected genome-wide mutation information of virulent strains and the severity of COVID-19 pneumonia in patients varying depending on their clinical status. Important protein mutations and untranslated region mutations were extracted using machine learning methods. First, through Boruta and fou

SUBMITTER: Huang F 

PROVIDER: S-EPMC9225528 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature