End-to-End 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) End-to-End Multi-channel Neural Networks for Predicting Influenza a Virus Hosts and Antigenic Types. In: 14th International Conference on Knowledge Discovery and Information Retrieval, 2022-10-24 - 2022-10-26.

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Official URL: http://dx.doi.org/10.5220/0011526300003335

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.

Item Type:	Conference or Workshop Item (Unspecified)
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	22 Jun 2023 07:46
Last Modified:	26 Apr 2024 21:13
DOI:	10.5220/0011526300003335
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3171190