Open Access

ARTICLE

Intelligent Approach for Clustering Mutations’ Nature of COVID-19 Genome

Ankur Dumka¹, Parag Verma², Rajesh Singh³, Anuj Bhardwaj⁴, Khalid Alsubhi⁵, Divya Anand^6,7,*, Irene Delgado Noya^7,8, Silvia Aparicio Obregon^7,9

1 Computer Science and Engineering, Women Institute of Technology, Uttarakhand, 248007, India
2 Chitkara University Institute of Engineering and Technology, Chitkara University Punjab, Punjab, 140401, India
3 Division of Innovation & Entrepreneurship, Lovely Professional University, Punjab, 144411, India
4 Computer Science and Engineering, Chandigarh University, Punjab, 140413, India
5 Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 37848, Saudi Arabia
6 Computer Science and Engineering, Lovely Professional University, Punjab, 144411, India
7 Higher Polytechnic School, Universidad Europea del Atlántico, Santander, 39011, Spain
8 Universidad Internacional Iberoamericana, Campeche, 24560, Mexico, C.P
9 Universidade Internacional do Cuanza Bairro Kaluanda, Bié, Angola

* Corresponding Author: Divya Anand. Email:

Computers, Materials & Continua 2022, 72(3), 4453-4466. https://doi.org/10.32604/cmc.2022.023974

Received 28 September 2021; Accepted 10 January 2022; Issue published 21 April 2022

Abstract

In December 2019, a group of people in Wuhan city of Hubei province of China were found to be affected by an infection called dark etiology pneumonia. The outbreak of this pneumonia infection was declared a deadly disease by the China Center for Disease Control and Prevention on January 9, 2020, named Novel Coronavirus 2019 (nCoV-2019). This nCoV-2019 is now known as COVID-19. There is a big list of infections of this coronavirus which is present in the form of a big family. This virus can cause several diseases that usually develop with a serious problem. According to the World Health Organization (WHO), 2019-nCoV has been placed as the modern generation of Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) coronaviruses, so COVID-19 can repeatedly change its internal genome structure to extend its existence. Understanding and accurately predicting the mutational properties of the genome structure of COVID-19 can form a good leadership role in preventing and fighting against coronavirus. In this research paper, an analytical approach has been presented which is based on the k-means cluster technique of machine learning to find the clusters over the mutational properties of the COVID-19 viruses’ complete genome. This method would be able to act as a promising tool to monitor and track pathogenic infections in their stable and local genetics/hereditary varieties. This paper identifies five main clusters of mutations with as best in most cases in the coronavirus that could help scientists and researchers develop disease control vaccines for the transformation of coronaviruses.

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