2018/02/26 by Woo Yong Choi, Choi, Woo Yong, Kyu Ye Song +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Medicine · #FOS: Biological sciences #FOS: Computer and information sciences #Genomics and Phylogenetic Studies #Identification and Quantification in Food #Influenza Virus Research Studies #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Quantitative Methods (q-bio.QM) #cs.LG #q-bio.QM #stat.ML
paper · pdf · doi:10.48550/arxiv.1802.09197
arxiv created 2018/02/26 · openalex publication_date 2018/02/26 · arxiv updated 2018/02/27 · openalex created_date 2018/03/06 · openalex updated_date 2026/07/28
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.