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The recent outbreak of COVID-19 pandemic is attributed to cross-species transmission of new coronavirus from bats to humans through unknown intermediate hosts, and the essence of the transmission is closely related to the mutations in coronaviruses. Furthermore, the effort to develop the vaccines against coronaviruses always faces the challenge of unexpected mutations in coronaviruses. In fact, it is very difficult to predict the mutations in any virus and bacterium, although mutations are a process of evolution. Over years, we have been applied the neural network to predict the mutations in proteins from influenza A viruses in comparison with the predictions using logistic regression. Our results are encouraging, but our approaches still need the improvements, for example, to upgrade to using machine learning and artificial intelligence instead of neural network. In this review, we summarize the rationales of neural network modelling, its strength and weakness, with the hope that we can apply the improved methods to predict the mutations in coronaviruses, thus to explore the origin of SARS-CoV-2, to find its intermediate host, and eventually to predict its mutations.

journal of physics. conference series

Application of neural network to predict mutations in proteins from influenza A viruses - A review of our approaches with implication for predicting mutations in coronaviruses