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

TL;DR

This paper introduces multi-channel neural networks that accurately predict influenza A virus hosts and subtypes from protein sequences, aiding rapid diagnosis and resource-limited settings.

Contribution

It presents a novel multi-channel neural network approach for predicting influenza A virus hosts and subtypes using hemagglutinin and neuraminidase sequences.

Findings

Effective on complete protein sequences

Performs well on incomplete sequences

Shows promise for rapid influenza prediction

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.