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Classification of Hepatitis Viruses from Sequencing Chromatograms Using Multiscale Permutation Entropy and Support Vector Machines

Department of Statistics, Yildiz Technical University, 34220 Istanbul, Turkey
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Entropy 2019, 21(12), 1149; https://doi.org/10.3390/e21121149
Received: 21 October 2019 / Revised: 21 November 2019 / Accepted: 23 November 2019 / Published: 25 November 2019
(This article belongs to the Section Entropy and Biology)
Classifying nucleic acid trace files is an important issue in molecular biology researches. For the purpose of obtaining better classification performance, the question of which features are used and what classifier is implemented to best represent the properties of nucleic acid trace files plays a vital role. In this study, different feature extraction methods based on statistical and entropy theory are utilized to discriminate deoxyribonucleic acid chromatograms, and distinguishing their signals visually is almost impossible. Extracted features are used as the input feature set for the classifiers of Support Vector Machines (SVM) with different kernel functions. The proposed framework is applied to a total number of 200 hepatitis nucleic acid trace files which consist of Hepatitis B Virus (HBV) and Hepatitis C Virus (HCV). While the use of statistical-based feature extraction methods allows representing the properties of hepatitis nucleic acid trace files with descriptive measures such as mean, median and standard deviation, entropy-based feature extraction methods including permutation entropy and multiscale permutation entropy enable quantifying the complexity of these files. The results indicate that using statistical and entropy-based features produces exceptionally high performances in terms of accuracies (reached at nearly 99%) in classifying HBV and HCV. View Full-Text
Keywords: hepatitis nucleic acid sequencing; permutation entropy; multiscale permutation entropy; support vector machines hepatitis nucleic acid sequencing; permutation entropy; multiscale permutation entropy; support vector machines
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Öz, E.; Aşkın, Ö.E. Classification of Hepatitis Viruses from Sequencing Chromatograms Using Multiscale Permutation Entropy and Support Vector Machines. Entropy 2019, 21, 1149.

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