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).
![[img]](http://eprints.nias.res.in/style/images/fileicons/text.png) |
Text
2023-Compression_Complexity_Measures-NithinNagaraj.pdf - Published Version Restricted to Registered users only Download (513kB) |
| Contribution | Name |
|---|
| 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 |
| Official URL: | https://www.mdpi.com/1099-4300/25/1/81 |
| Related URLs: | |
| Funders: | * |
| Projects: | * |
| DOI: | https://doi.org/10.3390/e25010081 |
| URI: | http://eprints.nias.res.in/id/eprint/2491 |
Actions (login required)
 |
View Item |
