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 four ranking algorithms (least absolute shrinkage and selection operator, light gradient boosting machine, max-relevance and min-redundancy, and Monte Carlo feature selection), mutations that were highly correlated with the clinical status of the patients were screened out and sorted in four feature lists. Some mutations such as D614G and V1176F were shown to be associated with viral infectivity. Moreover, previously unreported mutations such as A320V of nsp14 and I164ILV of nsp14 were also identified, which suggests their potential roles. We then applied the incremental feature selection method to each feature list to construct efficient classifiers, which can be directly used to distinguish the clinical status of COVID-19 patients. Meanwhile, four sets of quantitative rules were set up, which can help us to more intuitively understand the role of each mutation in differentiating the clinical status of COVID-19 patients. Identified key mutations linked to virologic properties will help better understand the mechanisms of infection and will aid in the development of antiviral treatments.

Identification of uveitis-associated functions based on the feature selection analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment scores

“…Boruta is a widely used feature filtering method that relies on two concepts, i.e., shadow features and binomial distribution ( Carmona-Saez et al, 2007 ; Kursa and Rudnicki, 2010 ; Huang et al, 2022 ; Zhou et al, 2022 ). Features in Boruta compete with their random versions, which are created by random shuffling.…”

Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients

“…Features selected in the severity stage prediction is compared with the existing studies [19] , [37] , [56] . It shows that the common features between studies are very low.…”

“…However, few interesting studies used molecular data in machine learning related studies. It includes cell marker identification of COVID-19 severity [19] , severity related SARS-CoV-2 mutations [37] and methylation signatures for predicting severity of SARS-CoV-2 [56] . These studies identified several biomarkers related to SARS-CoV-2 severity in different level of gene expression.…”

Identifying anal and cervical tumorigenesis-associated methylation signaling with machine learning methods

“…Because of the enormous number of original methylation features and limited methylations related to anal or cervical carcinomas, Boruta was employed for initial filtering (13)(14)(15)(16).…”