Multi-channel neural networks for predicting influenza A virus hosts and antigenic types



Xu, Yanhua ORCID: 0000-0003-1028-9023 and Wojtczak, Dominik ORCID: 0000-0001-5560-0546
(2022) Multi-channel neural networks for predicting influenza A virus hosts and antigenic types. [Preprint]

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Abstract

Influenza occurs every season and occasionally causes pandemics. Despite its low mortality rate, influenza is a major public health concern, as it can be complicated by severe diseases like pneumonia. A fast, accurate and low-cost method to predict the origin host and subtype of influenza viruses could help reduce virus transmission and benefit resource-poor areas. In this work, we propose multi-channel neural networks to predict antigenic types and hosts of influenza A viruses with hemagglutinin and neuraminidase protein sequences. An integrated data set containing complete protein sequences were used to produce a pre-trained model, and two other data sets were used for testing the model's performance. One test set contained complete protein sequences, and another test set contained incomplete protein sequences. The results suggest that multi-channel neural networks are applicable and promising for predicting influenza A virus hosts and antigenic subtypes with complete and partial protein sequences.

Item Type: Preprint
Additional Information: Accepted for publication at IC3K (KDIR) 2022
Uncontrolled Keywords: q-bio.QM, q-bio.QM, 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: 15 Mar 2024 00:25
DOI: 10.48550/arxiv.2206.03823
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3161689