ArticleNonlinear dynamics2020
Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov's complexity and Shannon's information theories.
Article in Nonlinear dynamics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Zgli: A Pipeline for Clustering by Compression with Application to Patient Stratification in Spondyloarthritis.Sensors (Basel, Switzerland) · 2023Article
- In memory of Professor José António Tenreiro Machado (1957-2021).Nonlinear dynamics · 2022Article
- Complexity of COVID-19 Dynamics.Entropy (Basel, Switzerland) · 2021Article
- New applications related to Covid-19.Results in physics · 2021Article
- Advances in the computational analysis of SARS-COV2 genome.Nonlinear dynamics · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper tackles the information of 133 RNA viruses available in public databases under the light of several mathematical and computational tools. First, the formal concepts of distance metrics, Kolmogorov complexity and Shannon information are recalled. Second, the computational tools available presently for tackling and visualizing patterns embedded in datasets, such as the hierarchical clustering and the multidimensional scaling, are discussed. The synergies of the common application of the mathematical and computational resources are then used for exploring the RNA data, cross-evaluating the normalized compression distance, entropy and Jensen-Shannon divergence, versus representations in two and three dimensions. The results of these different perspectives give extra light in what concerns the relations between the distinct RNA viruses.
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