Open Access

ARTICLE

Predicting Genotype Information Related to COVID-19 for Molecular Mechanism Based on Computational Methods

Lejun Gong^1,2,*, Xingxing Zhang¹, Li Zhang³, Zhihong Gao⁴

1 Jiangsu Key Laboratory of Big Data Security & Intelligent Processing School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210046, China
2 Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province, Nanjing, 210046, China
3 College of Computer Science and Technology, Nanjing Forestry University, Nanjing, 210037, China
4 Zhejiang Engineering Research Center of Intelligent Medicine, Wenzhou, 325035, China

* Corresponding Author: Lejun Gong. Email:

(This article belongs to the Special Issue: Computer Methods in Bio-mechanics and Biomedical Engineering)

Computer Modeling in Engineering & Sciences 2021, 129(1), 31-45. https://doi.org/10.32604/cmes.2021.016622

Received 12 March 2021; Accepted 10 June 2021; Issue published 24 August 2021

Abstract

Novel coronavirus disease 2019 (COVID-19) is an ongoing health emergency. Several studies are related to COVID-19. However, its molecular mechanism remains unclear. The rapid publication of COVID-19 provides a new way to elucidate its mechanism through computational methods. This paper proposes a prediction method for mining genotype information related to COVID-19 from the perspective of molecular mechanisms based on machine learning. The method obtains seed genes based on prior knowledge. Candidate genes are mined from biomedical literature. The candidate genes are scored by machine learning based on the similarities measured between the seed and candidate genes. Furthermore, the results of the scores are used to perform functional enrichment analyses, including KEGG, interaction network, and Gene Ontology, for exploring the molecular mechanism of COVID-19. Experimental results show that the method is promising for mining genotype information to explore the molecular mechanism related to COVID-19.

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Keywords

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