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Avian Influenza breakouts cause millions of dollars in damage each yearglobally, especially in Asian countries such as China and South Korea. Theimpact magnitude of a breakout directly correlates to time required to fullyunderstand the influenza virus, particularly the interspecies pathogenicity.The procedure requires laboratory tests that require resources typicallylacking in a breakout emergency. In this study, we propose new quantitativemethods utilizing machine learning and deep learning to correctly classify hostspecies given raw DNA sequence data of the influenza virus, and provideprobabilities for each classification. The best deep learning models achievetop-1 classification accuracy of 47%, and top-3 classification accuracy of 82%,on a dataset of 11 host species classes.

AI4AI: Quantitative Methods for Classifying Host Species from Avian Influenza DNA Sequence