COVID-19 and SARS Virus Function Sites Classification with Machine Learning Methods

ABSTRACT

COVID-19 and SARS virus are two related coronaviruses. In recent years, the increasingly serious epidemic situation has become the focus of all human beings, and has brought a significant impact on daily life. So, we proposed a link analysis of the two viruses. We obtained all the required COVID-19 and SARS virus data from the Uniprot database website, and we preprocessed the data after obtaining the data. In the prediction of the binding site of the COVID-19 and SARS, it is to judge the validity between the two binding sites. In response to this problem, we used Adaboost, voting-classifier and SVM classifier, and compared different classifier strategies through experiments. Among them, Metal binding site can effectively improve the accuracy of protein binding site prediction, and the effect is more obvious. Provide assistance for bioinformatics research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.