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Online Predictor Using Machine Learning to Predict Novel Coronavirus and Other Pathogenic Viruses
[Image: see text] The problem of virus classification is always a subject of concern for virology or epidemiology over the decades. In this regard, a machine learning technique can be used to predict the novel coronavirus by considering its sequence. Thus, we are proposing a machine learning-based n...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9280959/ https://www.ncbi.nlm.nih.gov/pubmed/35847318 http://dx.doi.org/10.1021/acsomega.2c00215 |
Sumario: | [Image: see text] The problem of virus classification is always a subject of concern for virology or epidemiology over the decades. In this regard, a machine learning technique can be used to predict the novel coronavirus by considering its sequence. Thus, we are proposing a machine learning-based novel coronavirus prediction technique, called COVID-Predictor, where 1000 sequences of SARS-CoV-1, MERS-CoV, SARS-CoV-2, and other viruses are used to train a Naive Bayes classifier so that it can predict any unknown sequences of these viruses. The model has been validated using 10-fold cross-validation in comparison with other machine learning techniques. The results show the superiority of our predictor by achieving an average 99.7% accuracy on an unseen validation set of viruses. The same pre-trained model has been used to design a web-based application where sequences of unknown viruses can be uploaded to predict the novel coronavirus. |
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