Alignment-free Z-curve genomic cepstral coefficients and machine learning for classification of viruses
| dc.creator | Adetiba, E., Olugbara, O.O., Taiwo, T.B., Adebiyi, M.O., Badejo, J. A., Akanle, M.B., Matthews, V.O. | |
| dc.date | 2018 | |
| dc.date.accessioned | 2025-04-01T17:57:05Z | |
| dc.description | 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. | |
| dc.format | text/html | |
| dc.identifier | http://eprints.covenantuniversity.edu.ng/11668/ | |
| dc.identifier.uri | https://repository.covenantuniversity.edu.ng/handle/123456789/41502 | |
| dc.language | en | |
| dc.publisher | Springer Verlag | |
| dc.subject | QA75 Electronic computers. Computer science | |
| dc.title | Alignment-free Z-curve genomic cepstral coefficients and machine learning for classification of viruses | |
| dc.type | Book Section |