{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:01:46Z","timestamp":1772136106205,"version":"3.50.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T00:00:00Z","timestamp":1626307200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T00:00:00Z","timestamp":1626307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    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 (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/www.gisaid.org\/\">https:\/\/www.gisaid.org\/<\/jats:ext-link>\n                    ), 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.\n                  <\/jats:p>","DOI":"10.1038\/s41598-021-93757-w","type":"journal-article","created":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T06:03:50Z","timestamp":1626329030000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A hybrid computational framework for intelligent inter-continent SARS-CoV-2 sub-strains characterization and prediction"],"prefix":"10.1038","volume":"11","author":[{"given":"Moses Effiong","family":"Ekpenyong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mercy Ernest","family":"Edoho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Udoinyang Godwin","family":"Inyang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faith-Michael","family":"Uzoka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Itemobong Samuel","family":"Ekaidem","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anietie Effiong","family":"Moses","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martins Ochubiojo","family":"Emeje","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youtchou Mirabeau","family":"Tatfeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ifiok James","family":"Udo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"EnoAbasi Deborah","family":"Anwana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oboso Edem","family":"Etim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph Ikim","family":"Geoffery","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emmanuel Ambrose","family":"Dan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,15]]},"reference":[{"issue":"2","key":"93757_CR1","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.jnma.2020.03.015","volume":"112","author":"EP Mitchell","year":"2020","unstructured":"Mitchell, E. P. Corona virus: Global pandemic causing world-wide shutdown. J. Natl Med. Assoc. 112(2), 113\u2013114. https:\/\/doi.org\/10.1016\/j.jnma.2020.03.015 (2020).","journal-title":"J. Natl Med. Assoc."},{"issue":"10229","key":"93757_CR2","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1016\/S0140-6736(20)30673-5","volume":"395","author":"J Bedford","year":"2020","unstructured":"Bedford, J. et al. COVID-19: Towards controlling of a pandemic. The Lancet 395(10229), 1015\u20131018. https:\/\/doi.org\/10.1016\/S0140-6736(20)30673-5 (2020).","journal-title":"The Lancet"},{"issue":"1","key":"93757_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-19818-2","volume":"11","author":"L van Dorp","year":"2020","unstructured":"van Dorp, L. et al. No evidence for increased transmissibility from recurrent mutations in SARS-CoV-2. Nat. Commun. 11(1), 1\u20138. https:\/\/doi.org\/10.1038\/s41467-020-19818-2 (2020).","journal-title":"Nat. Commun."},{"key":"93757_CR4","doi-asserted-by":"crossref","unstructured":"Chen, J., Wang, R., Wang, M., & Wei, G. W. Mutations strengthened SARS-CoV-2 infectivity. (2020). https:\/\/arxiv.org\/abs\/2005.14669","DOI":"10.1016\/j.jmb.2020.07.009"},{"issue":"5","key":"93757_CR5","doi-asserted-by":"publisher","first-page":"324","DOI":"10.3390\/pathogens9050324","volume":"9","author":"T Koyama","year":"2020","unstructured":"Koyama, T., Weeraratne, D., Snowdon, J. L. & Parida, L. Emergence of drift variants that may affect COVID-19 vaccine development and antibody treatment. Pathogens 9(5), 324. https:\/\/doi.org\/10.3390\/pathogens9050324 (2020).","journal-title":"Pathogens"},{"issue":"10","key":"93757_CR6","doi-asserted-by":"publisher","first-page":"e0241165","DOI":"10.1371\/journal.pone.0241165","volume":"15","author":"S Roy","year":"2020","unstructured":"Roy, S. & Ghosh, P. Factors affecting COVID-19 infected and death rates inform lockdown-related policymaking. PLoS ONE 15(10), e0241165. https:\/\/doi.org\/10.1371\/journal.pone.0241165 (2020).","journal-title":"PLoS ONE"},{"issue":"13","key":"93757_CR7","doi-asserted-by":"publisher","first-page":"2000305","DOI":"10.2807\/1560-7917.ES.2020.25.13.2000305","volume":"25","author":"P Stefanelli","year":"2020","unstructured":"Stefanelli, P. et al. Whole genome and phylogenetic analysis of two SARS-CoV-2 strains isolated in Italy in January and February 2020: Additional clues on multiple introductions and further circulation in Europe. Eurosurveillance 25(13), 2000305. https:\/\/doi.org\/10.2807\/1560-7917.ES.2020.25.13.2000305 (2020).","journal-title":"Eurosurveillance"},{"issue":"8","key":"93757_CR8","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1056\/NEJMoa2001017","volume":"382","author":"N Zhu","year":"2020","unstructured":"Zhu, N. et al. A novel coronavirus from patients with pneumonia in China, 2019. N. Engl. J. Med. 382(8), 727\u2013733. https:\/\/doi.org\/10.1056\/NEJMoa2001017 (2020).","journal-title":"N. Engl. J. Med."},{"key":"93757_CR9","doi-asserted-by":"publisher","first-page":"1012","DOI":"10.1093\/nsr\/nwaa036","volume":"7","author":"X Tang","year":"2020","unstructured":"Tang, X. et al. On the origin and continuing evolution of SARS-CoV-2. Natl. Sci. Rev. 7, 1012\u20131023. https:\/\/doi.org\/10.1093\/nsr\/nwaa036 (2020).","journal-title":"Natl. Sci. Rev."},{"issue":"83","key":"93757_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/gm487","volume":"5","author":"IR Wiechers","year":"2013","unstructured":"Wiechers, I. R., Perin, N. C. & Cook-Deegan, R. The emergence of commercial genomics: analysis of the rise of a biotechnology subsector during the Human Genome Project, 1990 to 2004. Genome Med. 5(83), 1\u20139. https:\/\/doi.org\/10.1186\/gm487 (2013).","journal-title":"Genome Med."},{"key":"93757_CR11","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.csbj.2019.11.002","volume":"18","author":"AM Giani","year":"2020","unstructured":"Giani, A. M., Gallo, G. R., Gianfranceschi, L. & Formenti, G. Long walk to genomics: History and current approaches to genome sequencing and assembly. Comput. Struct. Biotechnol. J. 18, 9\u201319. https:\/\/doi.org\/10.1016\/j.csbj.2019.11.002 (2020).","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"93757_CR12","doi-asserted-by":"publisher","first-page":"103795","DOI":"10.1016\/j.compbiomed.2020.103795","volume":"121","author":"AA Ardakani","year":"2020","unstructured":"Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N. & Mohammadi, A. Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Comput Biol Med. 121, 103795. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103795 (2020).","journal-title":"Comput Biol Med."},{"key":"93757_CR13","doi-asserted-by":"publisher","first-page":"103792","DOI":"10.1016\/j.compbiomed.2020.103792","volume":"121","author":"T Ozturk","year":"2020","unstructured":"Ozturk, T. et al. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. 121, 103792. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103792 (2020).","journal-title":"Comput. Biol. Med."},{"key":"93757_CR14","doi-asserted-by":"publisher","first-page":"104431","DOI":"10.1016\/j.jcv.2020.104431","volume":"128","author":"L Sun","year":"2020","unstructured":"Sun, L. et al. Combination of four clinical indicators predicts the severe\/critical symptom of patients infected COVID-19. J. Clin. Virol. 128, 104431. https:\/\/doi.org\/10.1016\/j.jcv.2020.104431 (2020).","journal-title":"J. Clin. Virol."},{"key":"93757_CR15","doi-asserted-by":"publisher","DOI":"10.1101\/2020.04.02.20051136","author":"J Wu","year":"2020","unstructured":"Wu, J. et al. Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results. MedRxiv https:\/\/doi.org\/10.1101\/2020.04.02.20051136 (2020).","journal-title":"MedRxiv"},{"key":"93757_CR16","unstructured":"MIT. Covid Tracing Tracker: A flood of coronavirus apps are tracking us. Now it's time to keep track of them. https:\/\/www.technologyreview.com\/2020\/05\/07\/1000961\/launching-mittr-covid-tracing-tracker\/. Accessed 20 Aug 2020."},{"key":"93757_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.109853","author":"MHDM Ribeiro","year":"2020","unstructured":"Ribeiro, M. H. D. M., da Silva, R. G., Mariani, V. C. & Coelho, L. D. S. Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. Chaos, Solitons Fractals. https:\/\/doi.org\/10.1016\/j.chaos.2020.109853 (2020).","journal-title":"Chaos, Solitons Fractals."},{"key":"93757_CR18","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-0180-7","author":"L Yan","year":"2020","unstructured":"Yan, L. et al. An interpretable mortality prediction model for COVID-19 patients. Nat. Mach. Intell. https:\/\/doi.org\/10.1038\/s42256-020-0180-7 (2020).","journal-title":"Nat. Mach. Intell."},{"key":"93757_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.bj.2020.05.001","author":"Y-Y Ke","year":"2020","unstructured":"Ke, Y.-Y. et al. Artificial intelligence approach fighting COVID-19 with repurposing drugs. Biomed. J. https:\/\/doi.org\/10.1016\/j.bj.2020.05.001 (2020).","journal-title":"Biomed. J."},{"key":"93757_CR20","doi-asserted-by":"publisher","first-page":"784","DOI":"10.1016\/j.csbj.2020.03.025","volume":"18","author":"BR Beck","year":"2020","unstructured":"Beck, B. R., Shin, B., Choi, Y., Park, S. & Kang, K. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Comput. Struct. Biotechnol. J. 18, 784\u2013790. https:\/\/doi.org\/10.1016\/j.csbj.2020.03.025 (2020).","journal-title":"Comput. Struct. Biotechnol. J."},{"issue":"5","key":"93757_CR21","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1016\/j.drudis.2020.03.019","volume":"25","author":"S Ekins","year":"2020","unstructured":"Ekins, S. et al. D\u00e9j\u00e0 vu: Stimulating open drug discovery for SARS-CoV-2. Drug. Discov. Today. 25(5), 928\u2013941. https:\/\/doi.org\/10.1016\/j.drudis.2020.03.019 (2020).","journal-title":"Drug. Discov. Today."},{"issue":"1","key":"93757_CR22","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1186\/s13059-017-1319-7","volume":"18","author":"A Zielezinski","year":"2017","unstructured":"Zielezinski, A., Vinga, S., Almeida, J. & Karlowski, W. M. Alignment-free sequence comparison: Benefits, applications, and tools. Genome Biol. 18(1), 186. https:\/\/doi.org\/10.1186\/s13059-017-1319-7 (2017).","journal-title":"Genome Biol."},{"issue":"4","key":"93757_CR23","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1093\/bioinformatics\/btg005","volume":"19","author":"S Vinga","year":"2003","unstructured":"Vinga, S. & Almeida, J. Alignment-free sequence comparison-a review. Bioinformatics 19(4), 513\u2013523. https:\/\/doi.org\/10.1093\/bioinformatics\/btg005 (2003).","journal-title":"Bioinformatics"},{"issue":"4","key":"93757_CR24","doi-asserted-by":"publisher","first-page":"e0232391","DOI":"10.1371\/journal.pone.0232391","volume":"15","author":"GS Randhawa","year":"2020","unstructured":"Randhawa, G. S. et al. Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID19 case study. PLoS ONE 15(4), e0232391. https:\/\/doi.org\/10.1371\/journal.pone.0232391 (2020).","journal-title":"PLoS ONE"},{"issue":"1","key":"93757_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s42003-021-01754-6","volume":"4","author":"R Wang","year":"2021","unstructured":"Wang, R. et al. Analysis of SARS-CoV-2 mutations in the United States suggests presence of four substrains and novel variants. Commun. Biol. 4(1), 1\u201314. https:\/\/doi.org\/10.1038\/s42003-021-01754-6 (2021).","journal-title":"Commun. Biol."},{"key":"93757_CR26","doi-asserted-by":"crossref","unstructured":"Wang, R., Chen, J., Gao, K., Hozumi, Y., Yin, C., & Wei, G. W. Characterizing SARS-CoV-2 mutations in the United States. (2020). https:\/\/arxiv.org\/abs\/2007.12692.","DOI":"10.21203\/rs.3.rs-49671\/v1"},{"key":"93757_CR27","doi-asserted-by":"publisher","DOI":"10.1101\/2021.02.07.21251262","author":"F Grabowski","year":"2021","unstructured":"Grabowski, F., Kochanczyk, M. & Lipniacki, T. L18F substrain of SARS-CoV-2 VOC-202012\/01 is rapidly spreading in England. MedRxiv https:\/\/doi.org\/10.1101\/2021.02.07.21251262 (2021).","journal-title":"MedRxiv"},{"key":"93757_CR28","doi-asserted-by":"publisher","DOI":"10.1101\/2020.06.08.20125534","author":"CS Richmond","year":"2020","unstructured":"Richmond, C. S., Sabin, A. P., Jobe, D. A., Lovrich, S. D. & Kenny, P. A. Interregional SARS-CoV-2 spread from a single introduction outbreak in a meat-packing plant in northeast Iowa. MedRxiv https:\/\/doi.org\/10.1101\/2020.06.08.20125534 (2020).","journal-title":"MedRxiv"},{"key":"93757_CR29","doi-asserted-by":"publisher","DOI":"10.1101\/2020.04.25.20079475","author":"K Somasundaram","year":"2020","unstructured":"Somasundaram, K., Mondal, M. & Lawarde, A. Genomics of Indian SARS-CoV-2: Implications in genetic diversity, possible origin and spread of virus. MedRxiv. https:\/\/doi.org\/10.1101\/2020.04.25.20079475 (2020).","journal-title":"MedRxiv."},{"key":"93757_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s41870-020-00495-9","author":"AMUD Khanday","year":"2020","unstructured":"Khanday, A. M. U. D., Rabani, S. T., Khan, Q. R., Rouf, N. & Din, M. M. U. Machine learning based approaches for detecting COVID-19 using clinical text data. Int. J. Inf. Technol. https:\/\/doi.org\/10.1007\/s41870-020-00495-9 (2020).","journal-title":"Int. J. Inf. Technol."},{"key":"93757_CR31","unstructured":"https:\/\/github.com\/Akibkhanday\/Meta-data-of-Coronavirus."},{"key":"93757_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.109917","author":"P Melin","year":"2020","unstructured":"Melin, P., Monica, J. C., Sanchez, D. & Castillo, O. Analysis of spatial spread relationships of coronavirus (COVID-19) pandemic in the world using self organizing maps. Chaos Solitons Fractals. https:\/\/doi.org\/10.1016\/j.chaos.2020.109917 (2020).","journal-title":"Chaos Solitons Fractals."},{"key":"93757_CR33","unstructured":"The Humanitarian Data Exchange (HDX), [Online] (2020). https:\/\/data.humdata.org\/dataset\/novel-coronavirus-2019-ncov-cases."},{"issue":"181","key":"93757_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/healthcare8020181","volume":"8","author":"P Melin","year":"2020","unstructured":"Melin, P., Monica, J. C., Sanchez, D. & Castillo, O. Multiple ensemble neural network models with fuzzy response aggregation for predicting COVID-19 time series: the case of Mexico. Healthcare 8(181), 1\u201313. https:\/\/doi.org\/10.3390\/healthcare8020181 (2020).","journal-title":"Healthcare"},{"key":"93757_CR35","doi-asserted-by":"publisher","first-page":"110242","DOI":"10.1016\/j.chaos.2020.110242","volume":"140","author":"O Castillo","year":"2020","unstructured":"Castillo, O. & Melin, P. Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic. Chaos Solitons Fractals 140, 110242. https:\/\/doi.org\/10.1016\/j.chaos.2020.110242 (2020).","journal-title":"Chaos Solitons Fractals"},{"key":"93757_CR36","doi-asserted-by":"publisher","DOI":"10.1101\/2021.01.20.427043","author":"A Lopez-Rincon","year":"2021","unstructured":"Lopez-Rincon, A. et al. Design of specific primer sets for the detection of variants of SARS-CoV-2 using artificial intelligence. BioRxiv https:\/\/doi.org\/10.1101\/2021.01.20.427043 (2021).","journal-title":"BioRxiv"},{"key":"93757_CR37","doi-asserted-by":"publisher","DOI":"10.1101\/2020.03.13.990242","author":"A Lopez-Rincon","year":"2020","unstructured":"Lopez-Rincon, A. et al. Accurate identification of sars-cov-2 from viral genome sequences using deep learning. BioRxiv. https:\/\/doi.org\/10.1101\/2020.03.13.990242 (2020).","journal-title":"BioRxiv."},{"key":"93757_CR38","unstructured":"Beijing Institute of Genomics, Chinese Academy of Science, China National Center for Bioinformation & National Genomics Data Center. https:\/\/bigd.big.ac.cn\/ncov\/?lang=en."},{"key":"93757_CR39","doi-asserted-by":"publisher","DOI":"10.1101\/2020.05.15.097741v1","author":"M Kaden","year":"2020","unstructured":"Kaden, M. et al. Analysis of SARS-CoV-2 RNA-sequences by interpretable machine learning models. BioRxiv https:\/\/doi.org\/10.1101\/2020.05.15.097741v1 (2020).","journal-title":"BioRxiv"},{"key":"93757_CR40","doi-asserted-by":"publisher","DOI":"10.1101\/2020.06.03.131987","author":"S Sawmya","year":"2020","unstructured":"Sawmya, S. et al. Analyzing hCov genome sequences: applying machine intelligence and beyond. BioRxiv https:\/\/doi.org\/10.1101\/2020.06.03.131987 (2020).","journal-title":"BioRxiv"},{"key":"93757_CR41","doi-asserted-by":"publisher","first-page":"109949","DOI":"10.1016\/j.chaos.2020.109949","volume":"1","author":"T Sun","year":"2020","unstructured":"Sun, T. & Wang, Y. Modeling COVID-19 epidemic in Heilongjiang Province, China. Chaos Solitons Fractals 1, 109949 (2020).","journal-title":"Chaos Solitons Fractals"},{"issue":"5","key":"93757_CR42","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1016\/j.bj.2020.08.003","volume":"43","author":"L Dey","year":"2020","unstructured":"Dey, L., Chakraborty, S. & Mukhopadhyay, A. Machine learning techniques for sequence-based prediction of viral\u2013host interactions between SARS-CoV-2 and human proteins. Biomed. J. 43(5), 438\u2013450. https:\/\/doi.org\/10.1016\/j.bj.2020.08.003 (2020).","journal-title":"Biomed. J."},{"key":"93757_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.bj.2020.08.003","author":"DE Gordon","year":"2020","unstructured":"Gordon, D. E. et al. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature https:\/\/doi.org\/10.1016\/j.bj.2020.08.003 (2020).","journal-title":"Nature"},{"key":"93757_CR44","doi-asserted-by":"publisher","first-page":"195263","DOI":"10.1109\/ACCESS.2020.3031387","volume":"8","author":"GS Dlamini","year":"2020","unstructured":"Dlamini, G. S. et al. Classification of COVID-19 and other pathogenic sequences: A dinucleotide frequency and machine learning approach. IEEE Access 8, 195263\u2013195273. https:\/\/doi.org\/10.1109\/ACCESS.2020.3031387 (2020).","journal-title":"IEEE Access"},{"key":"93757_CR45","doi-asserted-by":"publisher","first-page":"12331","DOI":"10.1038\/s41598-020-69342-y","volume":"10","author":"Y Wang","year":"2020","unstructured":"Wang, Y. et al. Human SARS-CoV-2 has evolved to reduce CG dinucleotide in its open reading frames. Sci. Rep. 10, 12331. https:\/\/doi.org\/10.1038\/s41598-020-69342-y (2020).","journal-title":"Sci. Rep."},{"key":"93757_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40249-020-00662-x","volume":"9","author":"MY Li","year":"2020","unstructured":"Li, M. Y., Li, L., Zhang, Y. & Wang, X. S. Expression of the SARS-CoV-2 cell receptor gene ACE2 in a wide variety of human tissues. Infect. Dis. Poverty 9, 1\u20137. https:\/\/doi.org\/10.1186\/s40249-020-00662-x (2020).","journal-title":"Infect. Dis. Poverty"},{"key":"93757_CR47","doi-asserted-by":"publisher","first-page":"1580","DOI":"10.1002\/jmv.25832","volume":"92","author":"M Hussain","year":"2020","unstructured":"Hussain, M. et al. Structural variations in human ACE2 may influence its binding with SARS-CoV-2 spike protein. J. Med. Virol. 92, 1580\u20131586. https:\/\/doi.org\/10.1002\/jmv.25832 (2020).","journal-title":"J. Med. Virol."},{"key":"93757_CR48","doi-asserted-by":"publisher","unstructured":"Edoho, M. E., Ekpenyong, M. E., Momodu, A. B., & Joseph, G. Mining the human metabolome for precision oncology research. in Proceedings of the 4th International Conference on Medical and Health Informatics, 8\u201317. (2020). https:\/\/doi.org\/10.1145\/3418094.3418123","DOI":"10.1145\/3418094.3418123"},{"issue":"12","key":"93757_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5815\/ijmecs.2019.12.01","volume":"11","author":"UG Inyang","year":"2019","unstructured":"Inyang, U. G., Eyoh, I. J., Robinson, S. A. & Udo, E. N. Visual association analytics approach to predictive modelling of students\u2019 academic performance. Int. J. Modern Educ. Comput. Sci. 11(12), 1\u201313. https:\/\/doi.org\/10.5815\/ijmecs.2019.12.01 (2019).","journal-title":"Int. J. Modern Educ. Comput. Sci."},{"issue":"2","key":"93757_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/S1469026820500121","volume":"19","author":"UG Inyang","year":"2020","unstructured":"Inyang, U. G., Akpan, E. E. & Akinyokun, O. C. A hybrid machine learning approach for flood risk assessment and classification. Int. J. Comput. Intell. Appl. 19(2), 1\u201320. https:\/\/doi.org\/10.1142\/S1469026820500121 (2020).","journal-title":"Int. J. Comput. Intell. Appl."},{"issue":"4","key":"93757_CR51","doi-asserted-by":"publisher","first-page":"37","DOI":"10.5539\/cis.v6n4p37","volume":"6","author":"UG Inyang","year":"2013","unstructured":"Inyang, U. G. & Joshua, E. E. Fuzzy clustering of students\u2019 data repository for at-risks students\u2019 identification and monitoring. Comput. Inf. Sci. 6(4), 37\u201350. https:\/\/doi.org\/10.5539\/cis.v6n4p37 (2013).","journal-title":"Comput. Inf. Sci."},{"key":"93757_CR52","doi-asserted-by":"publisher","unstructured":"Ekpenyong, M. E., & Inyang, U. G. Unsupervised mining of under-resourced speech corpora for tone features classification. in 2016 International Joint Conference on Neural Networks (IJCNN) (2374\u20132381). IEEE. (2016). https:\/\/doi.org\/10.1109\/IJCNN.2016.7727494","DOI":"10.1109\/IJCNN.2016.7727494"},{"issue":"7798","key":"93757_CR53","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1038\/s41586-020-2008-3","volume":"579","author":"F Wu","year":"2020","unstructured":"Wu, F. et al. Severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1, complete genome. Nature 579(7798), 265\u2013269 (2020).","journal-title":"Nature"},{"issue":"suppl 2","key":"93757_CR54","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1093\/nar\/gkq291","volume":"38","author":"F Abascal","year":"2010","unstructured":"Abascal, F., Zardoya, R. & Telford, M. J. TranslatorX: multiple alignment of nucleotide sequences guided by amino acid translations. Nucleic Acids Res. 38(suppl 2), 7\u201313. https:\/\/doi.org\/10.1093\/nar\/gkq291 (2010).","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"93757_CR55","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1109\/72.80208","volume":"1","author":"J Kangas","year":"1990","unstructured":"Kangas, J., Kohonen, T. & Laaksonen, J. Variants of self-organizing maps. IEEE Trans. Neural Netw. 1(1), 93\u201399. https:\/\/doi.org\/10.1109\/72.80208 (1990).","journal-title":"IEEE Trans. Neural Netw."},{"key":"93757_CR56","unstructured":"Vesanto, J. and Ahola, J. Hunting for correlations in data using the self-organizing map. in Proceeding of the International ICSC Congress on Computational Intelligence Methods and Applications, 279\u2013285. (1999)."},{"key":"93757_CR57","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/1758-2946-6-10","volume":"6","author":"D Krstajic","year":"2014","unstructured":"Krstajic, D. et al. Cross-validation pitfalls when selecting and assessing regression and classification models. J. Cheminform. 6, 10. https:\/\/doi.org\/10.1186\/1758-2946-6-10 (2014).","journal-title":"J. Cheminform."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-93757-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-93757-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-93757-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T06:16:56Z","timestamp":1670048216000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-93757-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,15]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["93757"],"URL":"https:\/\/doi.org\/10.1038\/s41598-021-93757-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-88429\/v3","asserted-by":"object"},{"id-type":"doi","id":"10.21203\/rs.3.rs-88429\/v2","asserted-by":"object"},{"id-type":"doi","id":"10.21203\/rs.3.rs-88429\/v1","asserted-by":"object"}]},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,15]]},"assertion":[{"value":"1 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"14558"}}