An Analytical Study of COVID-19 Dataset Using Graph-Based Clustering Algorithms
5th International Conference on Smart Computing and Informatics, SCI 2021
; 282:1-15, 2022.
Article
in English
| Scopus | ID: covidwho-1826285
ABSTRACT
COrona VIrus Disease abbreviated as COVID-19 is a novel virus which is initially identified in Wuhan of China in December of 2019, and now, this deadly disease has spread all over the world. According to World Health Organization (WHO), a total of 3,124,905 people died from 2019 to 2021, April. In this case, many methods, AI-based techniques, and machine learning algorithms, have been researched and are being used to save people from this pandemic. The SARS-CoV and the 2019-nCoV, SARS-CoV-2 virus invade our bodies, causing some differences in the structure of cell proteins. Protein–protein interaction (PPI) is an essential process in our cells and plays a very important role in the development of medicines and gives ideas about the disease. In this study, we performed clustering on PPI networks generated from 92 genes of the COVID-19 dataset. We have used three graph-based clustering algorithms to give intuition to the analysis of clusters. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Covid-19; Graph-based clustering; Markov chain clustering; Protein-protein interactions; STRING; Clustering algorithms; Diseases; Graphic methods; Learning algorithms; Machine learning; Markov processes; Proteins; Analytical studies; Cell proteins; Clusterings; Machine learning algorithms; Virus disease; World Health Organization; SARS
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
5th International Conference on Smart Computing and Informatics, SCI 2021
Year:
2022
Document Type:
Article
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