A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction.

Autor: Ekpenyong ME; Department of Computer Science, University of Uyo, P.M.B. 1017, Uyo, 520003, Nigeria. mosesekpenyong@uniuyo.edu.ng.; Centre for Research and Development, University of Uyo, P.M.B. 1017, Uyo, 520003, Nigeria. mosesekpenyong@uniuyo.edu.ng., Edoho ME; Department of Computer Science, University of Uyo, P.M.B. 1017, Uyo, 520003, Nigeria., Inyang UG; Department of Computer Science, University of Uyo, P.M.B. 1017, Uyo, 520003, Nigeria., Uzoka FM; Department of Mathematics and Computing, Mount Royal University, 4825 Mt Royal Gate SW, Calgary, AB, T3E 6K6, Canada., Ekaidem IS; College of Health Sciences, University of Uyo, P.M.B. 1017, Uyo, 520003, Nigeria., Moses AE; College of Health Sciences, University of Uyo, P.M.B. 1017, Uyo, 520003, Nigeria., Emeje MO; National Institute for Pharmaceutical Research and Development (NIPRD), Plot 942, Cadastral Zone C16, Idu, Industrial District, Abuja, FCT, Nigeria., Tatfeng YM; College of Health Sciences, Niger Delta University, Wilberforce Island, P.M.B. 071, Amassama, 560103, Nigeria., Udo IJ; Department of Computer Science, University of Uyo, P.M.B. 1017, Uyo, 520003, Nigeria., Anwana ED; Department of Botany and Ecological Studies, University of Uyo, P.M.B. 1017, Uyo, 520003, Nigeria., Etim OE; Department of Biochemistry, University of Uyo, P.M.B. 1017, Uyo, 520003, Nigeria., Geoffery JI; Department of Computer Science, University of Uyo, P.M.B. 1017, Uyo, 520003, Nigeria., Dan EA; Department of Computer Science, University of Uyo, P.M.B. 1017, Uyo, 520003, Nigeria.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2021 Jul 15; Vol. 11 (1), pp. 14558. Date of Electronic Publication: 2021 Jul 15.
DOI: 10.1038/s41598-021-93757-w
Abstrakt: 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.
(© 2021. The Author(s).)
Databáze: MEDLINE
Externí odkaz: