Open Access

ARTICLE

Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’ Response Against COVID Variants

by Hassam Tahir¹, Muhammad Shahbaz Khan¹, Fawad Ahmed², Abdullah M. Albarrak³, Sultan Noman Qasem³, Jawad Ahmad^4,*

1 Department of Electrical Engineering, HITEC University, Taxila, 47080, Pakistan
2 Department of Cyber Security, Pakistan Navy Engineering College, NUST, Karachi, 75350, Pakistan
3 Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
4 School of Computing, Edinburgh Napier University, Edinburgh, EH10 5DT, U.K

* Corresponding Author: Jawad Ahmad. Email:

Computers, Materials & Continua 2023, 75(2), 3517-3535. https://doi.org/10.32604/cmc.2023.035410

Received 20 August 2022; Accepted 29 January 2023; Issue published 31 March 2023

Abstract

The COVID-19 outbreak began in December 2019 and was declared a global health emergency by the World Health Organization. The four most dominating variants are Beta, Gamma, Delta, and Omicron. After the administration of vaccine doses, an eminent decline in new cases has been observed. The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies. However, strong variants like Delta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination. Therefore, it is indispensable to study, analyze and most importantly, predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons. In this regard, machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes. In this study, prediction of T-cells Epitopes’ response was conducted for vaccinated and unvaccinated people for Beta, Gamma, Delta, and Omicron variants. The dataset was divided into two classes, i.e., vaccinated and unvaccinated, and the predicted response of T-cell Epitopes was divided into three categories, i.e., Strong, Impaired, and Over-activated. For the aforementioned prediction purposes, a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers. Furthermore, the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach. Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error.

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Keywords

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