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, S. and Matthews, V.O. and Adebiyi, M.O. and Adebiyi, E.F. (2017) 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 Verlag, pp. 155-164.

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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. © Springer International Publishing AG 2017.

Item Type:	Book Section
Additional Information:	cited By 1; Conference of 5th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2017 ; Conference Date: 26 April 2017 Through 28 April 2017; Conference Code:190729
Uncontrolled Keywords:	Bioinformatics; Biomedical engineering; Computational efficiency; Genes; Pathogens; Viruses, Chaos game representation; Classification accuracy; Experimental investigations; FCGR; Genome sequences; Memory utilization; Sequences; Training and testing, Classification (of information)
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 10:03
Last Modified:	03 May 2019 11:45
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/11674

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