Open AccessA Machine Learning Method for Differentiating and Predicting Human‐Infective Coronavirus Based on Physicochemical Features and Composition of the Spike Protein
Author(s) -
Chao WANG,
Quan ZOU
Publication year - 2021
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2021.06.003
Subject(s) - spike (software development) , support vector machine , computer science , artificial intelligence , covid-19 , machine learning , warning system , coronavirus , pattern recognition (psychology) , computational biology , biology , medicine , pathology , software engineering , disease , infectious disease (medical specialty) , telecommunications
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.