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Published in:
2018 | OriginalPaper | Chapter
Alignment-Free Z-Curve Genomic Cepstral Coefficients and Machine Learning for Classification of Viruses
Authors : Emmanuel Adetiba, Oludayo O. Olugbara, Tunmike B. Taiwo, Marion O. Adebiyi, Joke A. Badejo, Matthew B. Akanle, Victor O. Matthews
Published in: Bioinformatics and Biomedical Engineering
Publisher: Springer International Publishing
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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.