  The Influenza type A virus can be considered as one of the most severe
viruses that can infect multiple species with often fatal consequences to the
hosts. The Haemagglutinin (HA) gene of the virus has the potential to be a
target for antiviral drug development realised through accurate identification
of its sub-types and possible the targeted hosts. In this paper, to accurately
predict if an Influenza type A virus has the capability to infect human hosts,
by using only the HA gene, is therefore developed and tested. The predictive
model follows three main steps; (i) decoding the protein sequences into
numerical signals using EIIP amino acid scale, (ii) analysing these sequences
by using Discrete Fourier Transform (DFT) and extracting DFT-based features,
(iii) using a predictive model, based on Artificial Neural Networks and using
the features generated by DFT. In this analysis, from the Influenza Research
Database, 30724, 18236 and 8157 HA protein sequences were collected for Human,
Avian and Swine, respectively. Given this set of the proteins, the proposed
method yielded 97.36% (+- 0.04%), 97.26% (+- 0.26%), 0.978 (+- 0.004), 0.963
(+- 0.005) and 0.945 (+- 0.005) for the training accuracy validation accuracy,
precision, recall and Mathews Correlation Coefficient (MCC) respectively, based
on a 10-fold cross-validation. The classification model generated by using one
of the largest dataset, if not the largest, yields promising results that could
lead to early detection of such species and help develop precautionary
measurements for possible human infections.
