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17 May 2022 Prediction of mutations in H5N1 hemagglutinin from influenza A virus with and without ending codon
Shaomin Yan, Guang Wu
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Proceedings Volume 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022); 122593L (2022) https://doi.org/10.1117/12.2638874
Event: 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, 2022, Kunming, China
Abstract
The successful prediction of mutations and recombination is perhaps the most important step for the preparedness against any emerging diseases. Our group had developed a model for the prediction of possible mutations in H5N1 hemagglutinin from influenza A virus. However, much more effort is needed to enhance the predictability. In this study, we include the RNA STOP codon from hemagglutinin for prediction because influenza A virus is a RNA virus and mutations occurred in RNA STOP codon. A total of 429 H5N1 hemagglutinins of influenza A viruses are used in a logistic regression model because it can include the interaction of two independent variables for comparison between the predictions including RNA STOP codons and the predictions without RNA STOP codons. The results demonstrated that the predictions with RNA STOP codons are better than the predictions without RNA STOP codons. Thus, the inclusion of RNA STOP codons does enhance the predictability.
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Shaomin Yan and Guang Wu "Prediction of mutations in H5N1 hemagglutinin from influenza A virus with and without ending codon", Proc. SPIE 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), 122593L (17 May 2022); https://doi.org/10.1117/12.2638874
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KEYWORDS
Neural networks
Proteins
Viruses
Modeling
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