  The Influenza virus can be considered as one of the most severe viruses that
can infect multiple species with often fatal consequences to the hosts. The
Hemagglutinin (HA) gene of the virus can be a target for antiviral drug
development realised through accurate identification of its sub-types and
possible the targeted hosts. This paper focuses on accurately predicting if an
Influenza type A virus can infect specific hosts, and more specifically, Human,
Avian and Swine hosts, using only the protein sequence of the HA gene. In more
detail, we propose encoding the protein sequences into numerical signals using
the Hydrophobicity Index and subsequently utilising a Convolutional Neural
Network-based predictive model. The Influenza HA protein sequences used in the
proposed work are obtained from the Influenza Research Database (IRD).
Specifically, complete and unique HA protein sequences were used for avian,
human and swine hosts. The data obtained for this work was 17999 human-host
proteins, 17667 avian-host proteins and 9278 swine-host proteins. Given this
set of collected proteins, the proposed method yields as much as 10% higher
accuracy for an individual class (namely, Avian) and 5% higher overall accuracy
than in an earlier study. It is also observed that the accuracy for each class
in this work is more balanced than what was presented in this earlier study. As
the results show, the proposed model can distinguish HA protein sequences with
high accuracy whenever the virus under investigation can infect Human, Avian or
Swine hosts.
