Characterization of evolutionary changes in hemagglutinin of influenza H1N1 virus: a computational analysis

Rehman, Zaira; Zafar, Rehan; Amir, Uzma; Niazi, Umar; Fahim, Ammad

doi:10.1007/s13337-015-0294-4

Cited by 1 publication

(1 citation statement)

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“…Additionally, as the authors concluded, classification and characterisation of all the sequences with the proposed methods, was difficult [5]- [7], thus a more advanced method is needed. Computational studies exist that tries to characterise and analyse the Influenza type A with promising results [8]. In the proposed method a computational, Artificial Neural Network (ANN) learning based approach is created to predict if a particular virus has the capability to infect humans, by only analysing the HA protein sequence.…”

Section: Introductionmentioning

confidence: 99%

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

Chrysostomou

Partaourides

Şeker

2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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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.

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Section: Introductionmentioning

confidence: 99%