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Identification of Pathogenic Viruses Using Genomic Cepstral Coefficients with Radial Basis Function Neural Network

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Advances in Nature and Biologically Inspired Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 419))

Abstract

Human populations are constantly inundated with viruses, some of which are responsible for various deadly diseases. Molecular biology approaches have been employed extensively to identify pathogenic viruses despite the limitations of the approaches. Nevertheless, recent advances in the next generation sequencing technologies have led to a surge in viral genome sequence databases with potentials for Bioinformatics based virus identification. In this study, we have utilised the Gaussian radial basis function neural network to identify pathogenic viruses. To validate the neural network model, samples of sequences of four different pathogenic viruses were extracted from the ViPR corpus. Electron-ion interaction pseudopotential scheme was used to encode the extracted sample sequences while cepstral analysis technique was applied to the encoded sequences to obtain a new set of genomic features, here called Genomic Cepstral Coefficients (GCCs). Experiments were performed to determine the potency of the GCCs to discriminate between different pathogenic viruses. Results show that GCCs are highly discriminating and gave good results when applied to identify some selected pathogenic viruses.

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Authors and Affiliations

  1. ICT and Society Research Group, Durban University of Technology, 1334, Durban, 4000, South Africa

    Emmanuel Adetiba, Oludayo O. Olugbara & Tunmike B. Taiwo

Authors
  1. Emmanuel Adetiba
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  2. Oludayo O. Olugbara
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  3. Tunmike B. Taiwo
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Corresponding author

Correspondence to Emmanuel Adetiba .

Editor information

Editors and Affiliations

  1. School of Mathematics, Statistics and CS, University of KwaZulu-Natal, Pietermaritzburg, South Africa

    Nelishia Pillay

  2. Sch of IT, Dept of CS, University of Pretoria, Pretoria, South Africa

    Andries P. Engelbrecht

  3. Sci N/w for Innova and Research Exc, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  4. Department of Computing Sciences,, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa

    Mathys C. du Plessis

  5. Faculty of Electrical Engineering and, Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Václav Snášel

  6. Fakulti Teknologi Maklumat dan Komunikas, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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© 2016 Springer International Publishing Switzerland

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Adetiba, E., Olugbara, O.O., Taiwo, T.B. (2016). Identification of Pathogenic Viruses Using Genomic Cepstral Coefficients with Radial Basis Function Neural Network. In: Pillay, N., Engelbrecht, A., Abraham, A., du Plessis, M., Snášel, V., Muda, A. (eds) Advances in Nature and Biologically Inspired Computing. Advances in Intelligent Systems and Computing, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-319-27400-3_25

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  • DOI: https://doi.org/10.1007/978-3-319-27400-3_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27399-0

  • Online ISBN: 978-3-319-27400-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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