A Machine Learning Method for Differentiating and Predicting Human-Infective Coronavirus Based on Physicochemical Features and Composition of the Spike Protein

WANG Chao; ZOU Quan

doi:10.1049/cje.2021.06.003

Volume 30 Issue 5

Sep. 2021

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WANG Chao and ZOU Quan, “A Machine Learning Method for Differentiating and Predicting Human-Infective Coronavirus Based on Physicochemical Features and Composition of the Spike Protein,” Chinese Journal of Electronics, vol. 30, no. 5, pp. 815-823, 2021, doi: 10.1049/cje.2021.06.003

Citation:

WANG Chao and ZOU Quan, “A Machine Learning Method for Differentiating and Predicting Human-Infective Coronavirus Based on Physicochemical Features and Composition of the Spike Protein,” Chinese Journal of Electronics, vol. 30, no. 5, pp. 815-823, 2021, doi: 10.1049/cje.2021.06.003

Citation:

WANG Chao and ZOU Quan, “A Machine Learning Method for Differentiating and Predicting Human-Infective Coronavirus Based on Physicochemical Features and Composition of the Spike Protein,” Chinese Journal of Electronics, vol. 30, no. 5, pp. 815-823, 2021, doi: 10.1049/cje.2021.06.003

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A Machine Learning Method for Differentiating and Predicting Human-Infective Coronavirus Based on Physicochemical Features and Composition of the Spike Protein

doi: 10.1049/cje.2021.06.003

WANG Chao¹,
ZOU Quan^{1,2
,
,}

1. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China;
2. Hainan Key Laboratory for Computational Science and Application, Hainan Normal University, Haikou 571158, China

Funds:

This paper is supported by the National Natural Science Foundation of China (No.61922020, No.61771331, No.62002051).

Received Date: 2020-09-11
Available Online: 2021-09-02

Abstract

Abstract

Several Coronaviruses (CoVs) are epidemic pathogens that cause severe respiratory syndrome and are associated with significant morbidity and mortality. In this paper, a machine learning method was developed for predicting the risk of human infection posed by CoVs as an early warning system. The proposed Spike-SVM (Support vector machine) model achieved an accuracy of 97.36% for Human-infective CoV (HCoV) and Nonhuman-infective CoV (Non-HCoV) classification. The top informative features that discriminate HCoVs and Non-HCoVs were identified. Spike-SVM is anticipated to be a useful bioinformatics tool for predicting the infection risk posed by CoVs to humans.
- Coronavirus,
- Virus-host association,
- Spike protein,
- Machine learning

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References(70)

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