  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.

Quantitative Methods;Machine Learning