Alignment-free Z-curve genomic cepstral coefficients and machine learning for classification of viruses

Adetiba, E. and Olugbara, O.O. and Taiwo, T.B. and Adebiyi, M.O. and Badejo, J. A. and Akanle, M.B. and Matthews, V.O. (2018) Alignment-free Z-curve genomic cepstral coefficients and machine learning for classification of viruses. In: Bioinformatics and Biomedical Engineering. Springer Verlag, pp. 290-301.

HTML Download (100Kb)

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. © 2018, Springer International Publishing AG, part of Springer Nature.

Item Type:	Book Section
Additional Information:	cited By 0; Conference of 6th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2018 ; Conference Date: 25 April 2018 Through 27 April 2018; Conference Code:213089
Uncontrolled Keywords:	Alignment; Artificial intelligence; Bioinformatics; Biomedical engineering; Classifiers; Digital signal processing; Genes; Health risks; Viruses, Alignment-free; Bayesian; Pathogenic; ViPR; ZCGCC, Learning systems
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science Faculty of Engineering, Science and Mathematics > School of Engineering Sciences
Depositing User:	Dr. Joke Badejo
Date Deposited:	18 Sep 2018 10:01
Last Modified:	03 May 2019 11:42
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/11668

Actions (login required)

View Item