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 

机构地区：[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

出　　处：《Chinese Journal of Electronics》2021年第5期815-823,共9页电子学报（英文版）

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

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.

关 键 词：CORONAVIRUS Virus-host association Spike protein Machine learning 

分 类 号：R181.8[医药卫生—流行病学] TP181[医药卫生—公共卫生与预防医学]