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

被引：21

作者：

Chao, Wang ^{[1
]}

Quan, Zou ^{[1
,2
]}

机构：

[1] Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu 610054, Peoples R China

[2] Hainan Normal Univ, Hainan Key Lab Computat Sci & Applicat, Haikou 571158, Peoples R China

来源：

CHINESE JOURNAL OF ELECTRONICS | 2021年 / 30卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Coronavirus; Virus-host association; Spike protein; Machine learning; FUNCTIONAL RECEPTOR; NEURAL-NETWORK; BINDING-SITES; IDENTIFICATION;

D O I：

10.1049/cje.2021.06.003

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：815 / 823

页数：9

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