Identifying Methylation Signatures and Rules for COVID-19 With Machine Learning Methods.

ABSTRACT: The occurrence of coronavirus disease 2019 (COVID-19) has become a serious challenge to global public health. Definitive and effective treatments for COVID-19 are still lacking, and targeted antiviral drugs are not available. In addition, viruses can regulate host innate immunity and antiviral processes through the epigenome to promote viral self-replication and disease progression. In this study, we first analyzed the methylation dataset of COVID-19 using the Monte Carlo feature selection method to obtain a feature list. This feature list was subjected to the incremental feature selection method combined with a decision tree algorithm to extract key biomarkers, build effective classification models and classification rules that can remarkably distinguish patients with or without COVID-19.

SUBMITTER: Li Z 

PROVIDER: S-EPMC9127386 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature