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Authors: Yanhua Xu and Dominik Wojtczak

Affiliation: Department of Computer Science, University of Liverpool, U.K.

Keyword(s): Machine Learning, Influenza Virus, Long Short-term Network, Convolutional Neural Network, Transformer.

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 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. (More)

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Paper citation in several formats:
Xu, Y. and Wojtczak, D. (2022). End-to-End Multi-channel Neural Networks for Predicting Influenza a Virus Hosts and Antigenic Types. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - KDIR; ISBN 978-989-758-614-9; ISSN 2184-3228, SciTePress, pages 40-50. DOI: 10.5220/0011526300003335

@conference{kdir22,
author={Yanhua Xu. and Dominik Wojtczak.},
title={End-to-End Multi-channel Neural Networks for Predicting Influenza a Virus Hosts and Antigenic Types},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - KDIR},
year={2022},
pages={40-50},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011526300003335},
isbn={978-989-758-614-9},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - KDIR
TI - End-to-End Multi-channel Neural Networks for Predicting Influenza a Virus Hosts and Antigenic Types
SN - 978-989-758-614-9
IS - 2184-3228
AU - Xu, Y.
AU - Wojtczak, D.
PY - 2022
SP - 40
EP - 50
DO - 10.5220/0011526300003335
PB - SciTePress

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