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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Nature Portfolio
2021
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oai:doaj.org-article:2dbfd35eab264c17aa310891ba1970e62021-12-02T18:30:45ZA hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction10.1038/s41598-021-93757-w2045-2322https://doaj.org/article/2dbfd35eab264c17aa310891ba1970e62021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93757-whttps://doaj.org/toc/2045-2322Abstract 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.Moses Effiong EkpenyongMercy Ernest EdohoUdoinyang Godwin InyangFaith-Michael UzokaItemobong Samuel EkaidemAnietie Effiong MosesMartins Ochubiojo EmejeYoutchou Mirabeau TatfengIfiok James UdoEnoAbasi Deborah AnwanaOboso Edem EtimJoseph Ikim GeofferyEmmanuel Ambrose DanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-25 (2021) |
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Medicine R Science Q Moses Effiong Ekpenyong Mercy Ernest Edoho Udoinyang Godwin Inyang Faith-Michael Uzoka Itemobong Samuel Ekaidem Anietie Effiong Moses Martins Ochubiojo Emeje Youtchou Mirabeau Tatfeng Ifiok James Udo EnoAbasi Deborah Anwana Oboso Edem Etim Joseph Ikim Geoffery Emmanuel Ambrose Dan A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction |
| 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 |
| author |
Moses Effiong Ekpenyong Mercy Ernest Edoho Udoinyang Godwin Inyang Faith-Michael Uzoka Itemobong Samuel Ekaidem Anietie Effiong Moses Martins Ochubiojo Emeje Youtchou Mirabeau Tatfeng Ifiok James Udo EnoAbasi Deborah Anwana Oboso Edem Etim Joseph Ikim Geoffery Emmanuel Ambrose Dan |
| author_facet |
Moses Effiong Ekpenyong Mercy Ernest Edoho Udoinyang Godwin Inyang Faith-Michael Uzoka Itemobong Samuel Ekaidem Anietie Effiong Moses Martins Ochubiojo Emeje Youtchou Mirabeau Tatfeng Ifiok James Udo EnoAbasi Deborah Anwana Oboso Edem Etim Joseph Ikim Geoffery Emmanuel Ambrose Dan |
| author_sort |
Moses Effiong Ekpenyong |
| 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 |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/2dbfd35eab264c17aa310891ba1970e6 |
| work_keys_str_mv |
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