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Online Predictor Using Machine Learning to Predict Novel Coronavirus and Other Pathogenic Viruses

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cris.lastimport.scopus2024-02-12T20:37:36Z
dc.abstract.enThe 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.
dc.affiliationUniwersytet Warszawski
dc.contributor.authorPlewczyński, Dariusz
dc.contributor.authorMaity, Debasree
dc.contributor.authorGhosh, Nimisha
dc.contributor.authorSaha, Indrajit
dc.contributor.authorSarkar, Jnanendra Prasad
dc.date.accessioned2024-01-25T15:44:23Z
dc.date.available2024-01-25T15:44:23Z
dc.date.issued2022
dc.description.financePublikacja bezkosztowa
dc.description.number27
dc.description.volume7
dc.identifier.doi10.1021/ACSOMEGA.2C00215
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/114624
dc.identifier.weblinkhttps://pubs.acs.org/doi/pdf/10.1021/acsomega.2c00215
dc.languageeng
dc.pbn.affiliationbiological sciences
dc.relation.ispartofACS Omega
dc.relation.pages23069-23074
dc.rightsClosedAccess
dc.sciencecloudnosend
dc.titleOnline Predictor Using Machine Learning to Predict Novel Coronavirus and Other Pathogenic Viruses
dc.typeJournalArticle
dspace.entity.typePublication