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

作     者：WANG Chao ZOU Quan WANG Chao;ZOU Quan

作者机构：Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Hainan Key Laboratory for Computational Science and Application Hainan Normal University 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2021年第30卷第5期

页      面：815-823页

核心收录：

学科分类：12[管理学] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 100401[医学-流行病与卫生统计学] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 10[医学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China (No.61922020  No.61771331  No.62002051) 

主　　题：Biomolecular structure, configuration, conformation, and active sites Biomolecular interactions, charge transfer complexes Biology and medical computing Coronavirus Virus-host association Spike protein Machine learning 

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.