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Alignment-Free Z-Curve Genomic Cepstral Coefficients and Machine Learning for Classification of Viruses

  • Emmanuel Adetiba
  • Oludayo O. Olugbara
  • Tunmike B. Taiwo
  • Marion O. Adebiyi
  • Joke A. Badejo
  • Matthew B. Akanle
  • Victor O. Matthews
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10813)

Abstract

Accurate detection of pathogenic viruses has become highly imperative. This is because viral diseases constitute a huge threat to human health and wellbeing on a global scale. However, both traditional and recent techniques for viral detection suffer from various setbacks. In codicil, some of the existing alignment-free methods are also limited with respect to viral detection accuracy. In this paper, we present the development of an alignment-free, digital signal processing based method for pathogenic viral detection named Z-Curve Genomic Cesptral Coefficients (ZCGCC). To evaluate the method, ZCGCC were computed from twenty six pathogenic viral strains extracted from the ViPR corpus. Naïve Bayesian classifier, which is a popular machine learning method was experimentally trained and validated using the extracted ZCGCC and other alignment-free methods in the literature. Comparative results show that the proposed ZCGCC gives good accuracy (93.0385%) and improved performance to existing alignment-free methods.

Keywords

Alignment-free Bayesian Classifier Naïve Pathogenic Virus ViPR ZCGCC 
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Notes

Acknowledgement

Funding to present this work at IWBBIO 2018 was provided by the Covenant University Centre for Research, Innovation and Development, Canaanland, Ota, Nigeria.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical and Information Engineering, College of EngineeringCovenant UniversityOtaNigeria
  2. 2.HRA, Institute for Systems ScienceDurban University of TechnologyDurbanSouth Africa
  3. 3.ICT and Society Research GroupDurban University of TechnologyDurbanSouth Africa
  4. 4.Department of Computer and Information Science, College of Science and TechnologyCovenant UniversityOtaNigeria

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