Prediction of Influenza A virus infections in humans using an Artificial Neural Network learning approach

TL;DR

This paper develops an ANN-based model using HA gene sequences and Fourier features to accurately predict Influenza A virus infection capability in humans, achieving over 97% accuracy.

Contribution

It introduces a novel approach combining EIIP encoding, DFT features, and neural networks for influenza host prediction using large-scale HA gene data.

Findings

Achieved over 97% accuracy in predicting human-infecting influenza strains.

Demonstrated the effectiveness of Fourier-based features in viral host classification.

Provided a scalable method for early detection of potentially infectious influenza viruses.

Abstract

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.