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.
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Acknowledgement
This work was supported by the Natural Science Foundation of China (No. 61902337), the fundamental Research Funds for the Central Universities, 2020QN89, Xuzhou science and technology plan project, KC19142, KC21047, Jiangsu Provincial Natural Science Foundation (No. SBK2019040953), Natural Science Fund for Colleges and Universities in Jiangsu Province (No. 19KJB520016) and Young talents of science and technology in Jiangsu.
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Wang, H. et al. (2022). COVID-19 and SARS Virus Function Sites Classification with Machine Learning Methods. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_64
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