Abstract
As of May 25, 2020, the novel coronavirus disease (called COVID-19) spread to more than 185 countries/regions with more than 348,000 deaths and more than 5,550,000 confirmed cases. In the bioinformatics area, one of the crucial points is the analysis of the virus nucleotide sequences using approaches such as data stream techniques and algorithms. However, to make feasible this approach, it is necessary to transform the nucleotide sequences string to numerical stream representation. Thus, the dataset provides four kinds of data stream representation (DSR) of SARS-CoV-2 virus nucleotide sequences. The dataset provides the DSR of 1557 instances of SARS-CoV-2 virus, 11540 other instances of other viruses from the Virus-Host DB dataset, and three instances of Riboviria viruses from NCBI (Betacoronavirus RaTG13, bat-SL-CoVZC45, and bat-SL-CoVZXC21).
Highlights
As of May 25, 2020, the novel coronavirus disease spread to more than 185 countries/regions with more than 348,000 deaths and more than 5,550,000 confirmed cases
One of the crucial points is the analysis of the virus nucleotide sequences using approaches such as data stream techniques and algorithms
Laboratory of Machine Learning and Intelligent Instrumentation, IMD/nPITI, Federal University of Rio Grande do Norte. https://data.mendeley.com/datasets/g5ktw4y4pz/2. These data are useful because they provide numeric representation of the COVID-2019 epidemic virus (SARS-CoV-2)
Summary
As of May 25, 2020, the novel coronavirus disease (called COVID-19) spread to more than 185 countries/regions with more than 348,000 deaths and more than 5,550,000 confirmed cases. One of the crucial points is the analysis of the virus nucleotide sequences using approaches such as data stream techniques and algorithms. The dataset provides four kinds of data stream representation (DSR) of SARS-CoV-2 virus nucleotide sequences. The data stream values were generated using Matlab.
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