A Machine Learning Method for Differentiating and Predicting Human‐Infective Coronavirus Based on Physicochemical Features and Composition of the Spike Protein
Chao Wang, ZOU Quan
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.
Topics & Concepts
Spike (software development)Support vector machineComputer scienceArtificial intelligenceCoronavirus disease 2019 (COVID-19)Machine learningWarning systemCoronavirusSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Pattern recognition (psychology)Computational biologyBiologyMedicinePathologySoftware engineeringInfectious disease (medical specialty)DiseaseTelecommunicationsMachine Learning in BioinformaticsSARS-CoV-2 and COVID-19 ResearchComputational Drug Discovery Methods