  Influenza poses a significant threat to public health, particularly among the
elderly, young children, and people with underlying dis-eases. The
manifestation of severe conditions, such as pneumonia, highlights the
importance of preventing the spread of influenza. An accurate and
cost-effective prediction of the host and antigenic sub-types of influenza A
viruses is essential to addressing this issue, particularly in
resource-constrained regions. In this study, we propose a multi-channel neural
network model to predict the host and antigenic subtypes of influenza A viruses
from hemagglutinin and neuraminidase protein sequences. Our model was trained
on a comprehensive data set of complete protein sequences and evaluated on
various test data sets of complete and incomplete sequences. The results
demonstrate the potential and practicality of using multi-channel neural
networks in predicting the host and antigenic subtypes of influenza A viruses
from both full and partial protein sequences.
