eprintid: 670 rev_number: 13 eprint_status: archive userid: 2 dir: disk0/00/00/06/70 datestamp: 2022-05-13 23:55:16 lastmod: 2023-07-12 23:30:31 status_changed: 2022-05-13 23:55:16 type: article metadata_visibility: show creators_name: Dumka, Ankur creators_name: Verma, Parag creators_name: Singh, Rajesh creators_name: Bhardwaj, Anuj creators_name: Alsubhi, Khalid creators_name: Anand, Divya creators_name: Delgado Noya, Irene creators_name: Aparicio Obregón, Silvia creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: divya.anand@uneatlantico.es creators_id: irene.delgado@uneatlantico.es creators_id: silvia.aparicio@uneatlantico.es title: Intelligent Approach for Clustering Mutations’ Nature of COVID-19 Genome ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: nCoV-2019; SARS-CoV-2; COVID-19; genome structure; etiology; COVID-19 mutations; COVID-19 genomes 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. date: 2022-04 publication: Computers, Materials & Continua volume: 72 number: 3 pagerange: 4453-4466 id_number: doi:10.32604/cmc.2022.023974 refereed: TRUE issn: 1546-2226 official_url: http://doi.org/10.32604/cmc.2022.023974 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés 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. metadata Dumka, Ankur; Verma, Parag; Singh, Rajesh; Bhardwaj, Anuj; Alsubhi, Khalid; Anand, Divya; Delgado Noya, Irene y Aparicio Obregón, Silvia mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, irene.delgado@uneatlantico.es, silvia.aparicio@uneatlantico.es (2022) Intelligent Approach for Clustering Mutations’ Nature of COVID-19 Genome. Computers, Materials & Continua, 72 (3). pp. 4453-4466. ISSN 1546-2226 document_url: http://repositorio.uneatlantico.es/id/eprint/670/1/TSP_CMC_47454.pdf