Title: AI4AI: Quantitative Methods for Classifying Host Species from Avian Influenza DNA Sequence,
Abstract: Avian Influenza breakouts cause millions of dollars in damage each year
globally, especially in Asian countries such as China and South Korea. The
impact magnitude of a breakout directly correlates to time required to fully
understand the influenza virus, particularly the interspecies pathogenicity.
The procedure requires laboratory tests that require resources typically
lacking in a breakout emergency. In this study, we propose new quantitative
methods utilizing machine learning and deep learning to correctly classify host
species given raw DNA sequence data of the influenza virus, and provide
probabilities for each classification. The best deep learning models achieve
top-1 classification accuracy of 47%, and top-3 classification accuracy of 82%,
on a dataset of 11 host species classes.