COVIDOUTCOME-estimating COVID severity based on mutation signatures in the SARS-CoV-2 genome.

ABSTRACT: Numerous studies demonstrate frequent mutations in the genome of SARS-CoV-2. Our goal was to statistically link mutations to severe disease outcome. We used an automated machine learning approach where 1594 viral genomes with available clinical follow-up data were used as the training set (797 'severe' and 797 'mild'). The best algorithm, based on random forest classification combined with the LASSO feature selection algorithm, was employed to the training set to link mutation signatures and outcome. The performance of the final model was estimated by repeated, stratified, 10-fold cross validation (CV) and then adjusted for multiple testing with Bootstrap Bias Corrected CV. We identified 26 protein and Untranslated Region (UTR) mutations significantly linked to severe outcome. The best cla

SUBMITTER: Nagy A 

PROVIDER: S-EPMC8106440 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature