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

Lejun Gong1,2,*, 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: email

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

Computer Modeling in Engineering & Sciences 2021, 129(1), 31-45.


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.


Cite This Article

Gong, L., Zhang, X., Zhang, L., Gao, Z. (2021). Predicting Genotype Information Related to COVID-19 for Molecular Mechanism Based on Computational Methods. CMES-Computer Modeling in Engineering & Sciences, 129(1), 31–45.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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