Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov's complexity and Shannon's information theories

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Machado, JAT (Author) Other; Rocha Neves, JM (Author) FMUP; Andrade, JP (Author) FMUP

Journal

Title: Nonlinear Dynamics

Vol. 101

Pages: 1731-1750

ISSN: 0924-090X

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 11 Citations

Scopus - 14 Citations

Other information

Authenticus ID: P-00S-DFV

DOI: 10.1007/s11071-020-05771-8

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 20