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Can I read Online Predictor Using Machine Learning to Predict Novel Coronavirus and Other Pathogenic Viruses on EtoBox?
Online Predictor Using Machine Learning to Predict Novel Coronavirus and Other Pathogenic Viruses by Jnanendra Prasad Sarkar; Indrajit Saha; Nimisha Ghosh; Debasree Maity; Dariusz Plewczynski is a Engineering article available to read on EtoBox.
What is Online Predictor Using Machine Learning to Predict Novel Coronavirus and Other Pathogenic Viruses about?
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.
Who reads Online Predictor Using Machine Learning to Predict Novel Coronavirus and Other Pathogenic Viruses?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Jnanendra Prasad Sarkar; Indrajit Saha; Nimisha Ghosh; Debasree Maity; Dariusz Plewczynski
- Published
- 2022
- Language
- EN
- Field
- Engineering (Physical Sciences)