Compression-Complexity Measures for Analysis and Classification of Coronaviruses

Munagala, Naga Venkata Trinath Sai and Amanchi, Prem Kumar and Balasubramanian, Karthi and Panicker, Athira and Nagaraj, Nithin (2023) Compression-Complexity Measures for Analysis and Classification of Coronaviruses. Entropy, 25 (1).

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Abstract: Finding a vaccine or specific antiviral treatment for a global pandemic of virus diseases (such as the ongoing COVID-19) requires rapid analysis, annotation and evaluation of metagenomic libraries to enable a quick and efficient screening of nucleotide sequences. Traditional sequence alignment methods are not suitable and there is a need for fast alignment-free techniques for sequence analysis. Information theory and data compression algorithms provide a rich set of mathematical and computational tools to capture essential patterns in biological sequences. In this study, we investigate the use of compression-complexity (Effort-to-Compress or ETC and Lempel-Ziv or LZ complexity) based distance measures for analyzing genomic sequences. The proposed distance measure is used to successfully reproduce the phylogenetic trees for a mammalian dataset consisting of eight species clusters, a set of coronaviruses belonging to group I, group II, group III, and SARS-CoV-1 coronaviruses, and a set of coronaviruses causing COVID-19 (SARS-CoV-2), and those not causing COVID-19. Having demonstrated the usefulness of these compression complexity measures, we employ them for the automatic classification of COVID-19-causing genome sequences using machine learning techniques. Two flavors of SVM (linear and quadratic) along with linear discriminant and fine K Nearest Neighbors classifer are used for classification. Using a data set comprising 1001 coronavirus sequences (causing COVID-19 and those not causing COVID-19), a classification accuracy of 98% is achieved with a sensitivity of 95% and a specificity of 99.8%. This work could be extended further to enable medical practitioners to automatically identify and characterize coronavirus strains and their rapidly growing mutants in a fast and efficient fashion.
Item Type: Journal Paper
Keywords: compression-complexity measures; Effort-to-Compress complexity; Lempel-Ziv complexity; distance measure; machine learning; COVID-19
Subjects: School of Humanities > Consciousness Studies
School of Humanities > Cognitive Science
Divisions: Schools > Humanities
Date Deposited: 11 Apr 2023 04:17
Last Modified: 11 Apr 2023 04:17
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