Prediction of antigenic peptides of SARS- CoV-2 pathogen using machine learning.

ABSTRACT: Antigenic peptides (APs), also known as T-cell epitopes (TCEs), represent the immunogenic segment of pathogens capable of inducing an immune response, making them potential candidates for epitope-based vaccine (EBV) design. Traditional wet lab methods for identifying TCEs are expensive, challenging, and time-consuming. Alternatively, computational approaches employing machine learning (ML) techniques offer a faster and more cost-effective solution. In this study, we present a robust XGBoost ML model for predicting TCEs of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus as potential vaccine candidates. The peptide sequences comprising TCEs and non-TCEs retrieved from Immune Epitope Database Repository (IEDB) were subjected to feature extraction process to extract thei

SUBMITTER: Bukhari SNH 

PROVIDER: S-EPMC11623221 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature