A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction
Abstract Whereas accelerated attention beclouded early stages of the coronavirus spread, knowledge of actual pathogenicity and origin of possible sub-strains remained unclear. By harvesting the Global initiative on Sharing All Influenza Data (GISAID) database ( https://www.gisaid.org/ ), between Dec...
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Online Access: | http://dx.doi.org/10.1038/s41598-021-93757-w http://www.nature.com/articles/s41598-021-93757-w.pdf http://www.nature.com/articles/s41598-021-93757-w |
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crspringernat:10.1038/s41598-021-93757-w 2023-05-15T14:09:57+02:00 A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction Ekpenyong, Moses Effiong Edoho, Mercy Ernest Inyang, Udoinyang Godwin Uzoka, Faith-Michael Ekaidem, Itemobong Samuel Moses, Anietie Effiong Emeje, Martins Ochubiojo Tatfeng, Youtchou Mirabeau Udo, Ifiok James Anwana, EnoAbasi Deborah Etim, Oboso Edem Geoffery, Joseph Ikim Dan, Emmanuel Ambrose 2021 http://dx.doi.org/10.1038/s41598-021-93757-w http://www.nature.com/articles/s41598-021-93757-w.pdf http://www.nature.com/articles/s41598-021-93757-w en eng Springer Science and Business Media LLC https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 CC-BY Scientific Reports volume 11, issue 1 ISSN 2045-2322 Multidisciplinary journal-article 2021 crspringernat https://doi.org/10.1038/s41598-021-93757-w 2022-01-04T16:33:35Z Abstract Whereas accelerated attention beclouded early stages of the coronavirus spread, knowledge of actual pathogenicity and origin of possible sub-strains remained unclear. By harvesting the Global initiative on Sharing All Influenza Data (GISAID) database ( https://www.gisaid.org/ ), between December 2019 and January 15, 2021, a total of 8864 human SARS-CoV-2 complete genome sequences processed by gender, across 6 continents (88 countries) of the world, Antarctica exempt, were analyzed. We hypothesized that data speak for itself and can discern true and explainable patterns of the disease. Identical genome diversity and pattern correlates analysis performed using a hybrid of biotechnology and machine learning methods corroborate the emergence of inter- and intra- SARS-CoV-2 sub-strains transmission and sustain an increase in sub-strains within the various continents, with nucleotide mutations dynamically varying between individuals in close association with the virus as it adapts to its host/environment. Interestingly, some viral sub-strain patterns progressively transformed into new sub-strain clusters indicating varying amino acid, and strong nucleotide association derived from same lineage. A novel cognitive approach to knowledge mining helped the discovery of transmission routes and seamless contact tracing protocol. Our classification results were better than state-of-the-art methods, indicating a more robust system for predicting emerging or new viral sub-strain(s). The results therefore offer explanations for the growing concerns about the virus and its next wave(s). A future direction of this work is a defuzzification of confusable pattern clusters for precise intra-country SARS-CoV-2 sub-strains analytics. Article in Journal/Newspaper Antarc* Antarctica Springer Nature (via Crossref) Scientific Reports 11 1 |
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Springer Nature (via Crossref) |
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crspringernat |
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English |
topic |
Multidisciplinary |
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Multidisciplinary Ekpenyong, Moses Effiong Edoho, Mercy Ernest Inyang, Udoinyang Godwin Uzoka, Faith-Michael Ekaidem, Itemobong Samuel Moses, Anietie Effiong Emeje, Martins Ochubiojo Tatfeng, Youtchou Mirabeau Udo, Ifiok James Anwana, EnoAbasi Deborah Etim, Oboso Edem Geoffery, Joseph Ikim Dan, Emmanuel Ambrose A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction |
topic_facet |
Multidisciplinary |
description |
Abstract Whereas accelerated attention beclouded early stages of the coronavirus spread, knowledge of actual pathogenicity and origin of possible sub-strains remained unclear. By harvesting the Global initiative on Sharing All Influenza Data (GISAID) database ( https://www.gisaid.org/ ), between December 2019 and January 15, 2021, a total of 8864 human SARS-CoV-2 complete genome sequences processed by gender, across 6 continents (88 countries) of the world, Antarctica exempt, were analyzed. We hypothesized that data speak for itself and can discern true and explainable patterns of the disease. Identical genome diversity and pattern correlates analysis performed using a hybrid of biotechnology and machine learning methods corroborate the emergence of inter- and intra- SARS-CoV-2 sub-strains transmission and sustain an increase in sub-strains within the various continents, with nucleotide mutations dynamically varying between individuals in close association with the virus as it adapts to its host/environment. Interestingly, some viral sub-strain patterns progressively transformed into new sub-strain clusters indicating varying amino acid, and strong nucleotide association derived from same lineage. A novel cognitive approach to knowledge mining helped the discovery of transmission routes and seamless contact tracing protocol. Our classification results were better than state-of-the-art methods, indicating a more robust system for predicting emerging or new viral sub-strain(s). The results therefore offer explanations for the growing concerns about the virus and its next wave(s). A future direction of this work is a defuzzification of confusable pattern clusters for precise intra-country SARS-CoV-2 sub-strains analytics. |
format |
Article in Journal/Newspaper |
author |
Ekpenyong, Moses Effiong Edoho, Mercy Ernest Inyang, Udoinyang Godwin Uzoka, Faith-Michael Ekaidem, Itemobong Samuel Moses, Anietie Effiong Emeje, Martins Ochubiojo Tatfeng, Youtchou Mirabeau Udo, Ifiok James Anwana, EnoAbasi Deborah Etim, Oboso Edem Geoffery, Joseph Ikim Dan, Emmanuel Ambrose |
author_facet |
Ekpenyong, Moses Effiong Edoho, Mercy Ernest Inyang, Udoinyang Godwin Uzoka, Faith-Michael Ekaidem, Itemobong Samuel Moses, Anietie Effiong Emeje, Martins Ochubiojo Tatfeng, Youtchou Mirabeau Udo, Ifiok James Anwana, EnoAbasi Deborah Etim, Oboso Edem Geoffery, Joseph Ikim Dan, Emmanuel Ambrose |
author_sort |
Ekpenyong, Moses Effiong |
title |
A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction |
title_short |
A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction |
title_full |
A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction |
title_fullStr |
A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction |
title_full_unstemmed |
A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction |
title_sort |
hybrid computational framework for intelligent inter-continent sars-cov-2 sub-strains characterization and prediction |
publisher |
Springer Science and Business Media LLC |
publishDate |
2021 |
url |
http://dx.doi.org/10.1038/s41598-021-93757-w http://www.nature.com/articles/s41598-021-93757-w.pdf http://www.nature.com/articles/s41598-021-93757-w |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_source |
Scientific Reports volume 11, issue 1 ISSN 2045-2322 |
op_rights |
https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1038/s41598-021-93757-w |
container_title |
Scientific Reports |
container_volume |
11 |
container_issue |
1 |
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1766281966669266944 |