Dive into Machine Learning Algorithms for Influenza Virus Host Prediction with Hemagglutinin Sequences

Xu, Yanhua ORCID: 0000-0003-1028-9023 and Wojtczak, Dominik ORCID: 0000-0001-5560-0546
(2022) Dive into Machine Learning Algorithms for Influenza Virus Host Prediction with Hemagglutinin Sequences. [Preprint]

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Influenza viruses mutate rapidly and can pose a threat to public health, especially to those in vulnerable groups. Throughout history, influenza A viruses have caused pandemics between different species. It is important to identify the origin of a virus in order to prevent the spread of an outbreak. Recently, there has been increasing interest in using machine learning algorithms to provide fast and accurate predictions for viral sequences. In this study, real testing data sets and a variety of evaluation metrics were used to evaluate machine learning algorithms at different taxonomic levels. As hemagglutinin is the major protein in the immune response, only hemagglutinin sequences were used and represented by position-specific scoring matrix and word embedding. The results suggest that the 5-grams-transformer neural network is the most effective algorithm for predicting viral sequence origins, with approximately 99.54% AUCPR, 98.01% F1 score and 96.60% MCC at a higher classification level, and approximately 94.74% AUCPR, 87.41% F1 score and 80.79% MCC at a lower classification level.

Item Type: Preprint
Additional Information: Published at BioSystems; V1: minor typo correction; V2 - V3: minor typo correction and more clarification in "Cross-validation" and "ER-PSSM" section
Uncontrolled Keywords: cs.LG, cs.LG
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 22 Aug 2022 12:24
Last Modified: 06 Jun 2024 02:14
DOI: 10.48550/arxiv.2207.13842
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3161688