Neural network to predict probabilistically possible mutations in hemagglutinins from Eurasia H1 influenza A virus

Shaomin Yan; Guang Wu

doi:10.1117/12.2604436

5 October 2021 Neural network to predict probabilistically possible mutations in hemagglutinins from Eurasia H1 influenza A virus

Shaomin Yan, Guang Wu

Author Affiliations +

Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 119111D (2021) https://doi.org/10.1117/12.2604436
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China

Abstract

The current COVID-19 pandemic continues with its new variants, whose mutations are unpredictable. Thus, how to predict mutations in viruses has profound meanings for vaccine and drug development as well as prevention measures. Currently the documented mutations in SARS-CoV-2 are not abundant yet, especially for making phylogenetic tree, it would be useful and easy to use the virus data with abundant mutations such as influenza A virus to build predictive model. In this study, a neural network with feedforward backpropagation algorithm is employed to predict the probabilistically possible mutation positions and mutated amino acids in hemagglutinins from Eurasia H1 influenza A virus. The study demonstrates an encouraging result and suggests the possibility to continue working along this research line.

Citation Download Citation

Shaomin Yan and Guang Wu "Neural network to predict probabilistically possible mutations in hemagglutinins from Eurasia H1 influenza A virus", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 119111D (5 October 2021); https://doi.org/10.1117/12.2604436

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