Experimental Investigation of Frequency Chaos Game Representation for in Silico and Accurate Classification of Viral Pathogens from Genomic Sequences

Adetiba, E. and Badejo, J. A. and Thakur, Surendra and Matthews, V. O. and Adebiyi, Marion O. and Adebiyi, E. F. (2018) Experimental Investigation of Frequency Chaos Game Representation for in Silico and Accurate Classification of Viral Pathogens from Genomic Sequences. In: Bioinformatics and Biomedical Engineering. Springer, Cham. ISBN 978-3-319-56147-9

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Abstract

This paper presents an experimental investigation to determine the efficacy and the appropriate order of Frequency Chaos Game Representation (FCGR) for accurate and in silico classification of pathogenic viruses. For this study, we curated genomic sequences of selected viral pathogens from the virus pathogen database and analysis resource corpus. The viral genomes were encoded using the first to seventh order FCGRs so as to produce training and testing genomic data features. Thereafter, four different kernels of naïve Bayes classifier were experimentally trained and tested with the generated FCGR genomic features. The performance result with the highest average classification accuracy of 98% was returned by the third and fourth order FCGRs. However, due to consideration for memory utilization, computational efficiency vis-à-vis classification accuracy, the third order FCGR is deemed suitable for accurate classification of viral pathogens from genome sequences. This provides a promising foundation for developing genomic based diagnostic toolkit that could be used to promptly address the global incidence of epidemics from pathogenic viruses.

Item Type:	Book Section
Uncontrolled Keywords:	Classification FCGR Genome GSP Naïve Bayes Pathogens Sequences Virus
Subjects:	T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	Mrs Hannah Akinwumi
Date Deposited:	27 Sep 2018 08:42
Last Modified:	27 Sep 2018 08:42
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/11926

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