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  1. Article ; Online: Relating SARS-CoV-2 variants using cellular automata imaging

    Luryane F. Souza / Tarcísio M. Rocha Filho / Marcelo A. Moret

    Scientific Reports, Vol 12, Iss 1, Pp 1-

    2022  Volume 6

    Abstract: Abstract We classify the main variants of the SARS-CoV-2 virus representing a given biological sequence coded as a symbolic digital sequence and by its evolution by a cellular automata with a properly chosen rule. The spike protein, common to all ... ...

    Abstract Abstract We classify the main variants of the SARS-CoV-2 virus representing a given biological sequence coded as a symbolic digital sequence and by its evolution by a cellular automata with a properly chosen rule. The spike protein, common to all variants of the SARS-CoV-2 virus, is then by the picture of the cellular automaton evolution yielding a visible representation of important features of the protein. We use information theory Hamming distance between different stages of the evolution of the cellular automaton for seven variants relative to the original Wuhan/China virus. We show that our approach allows to classify and group variants with common ancestors and same mutations. Although being a simpler method, it can be used as an alternative for building phylogenetic trees.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2022-06-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Relating SARS-CoV-2 variants using cellular automata imaging.

    Souza, Luryane F / Rocha Filho, Tarcísio M / Moret, Marcelo A

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 10297

    Abstract: We classify the main variants of the SARS-CoV-2 virus representing a given biological sequence coded as a symbolic digital sequence and by its evolution by a cellular automata with a properly chosen rule. The spike protein, common to all variants of the ... ...

    Abstract We classify the main variants of the SARS-CoV-2 virus representing a given biological sequence coded as a symbolic digital sequence and by its evolution by a cellular automata with a properly chosen rule. The spike protein, common to all variants of the SARS-CoV-2 virus, is then by the picture of the cellular automaton evolution yielding a visible representation of important features of the protein. We use information theory Hamming distance between different stages of the evolution of the cellular automaton for seven variants relative to the original Wuhan/China virus. We show that our approach allows to classify and group variants with common ancestors and same mutations. Although being a simpler method, it can be used as an alternative for building phylogenetic trees.
    MeSH term(s) COVID-19/genetics ; Cellular Automata ; Genome, Viral ; Humans ; Mutation ; Phylogeny ; SARS-CoV-2/genetics
    Language English
    Publishing date 2022-06-18
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-14404-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: New distance measure for comparing protein using cellular automata image.

    F Souza, Luryane / B de B Pereira, Hernane / M da Rocha Filho, Tarcisio / A S Machado, Bruna / A Moret, Marcelo

    PloS one

    2023  Volume 18, Issue 10, Page(s) e0287880

    Abstract: One of the first steps in protein sequence analysis is comparing sequences to look for similarities. We propose an information theoretical distance to compare cellular automata representing protein sequences, and determine similarities. Our approach ... ...

    Abstract One of the first steps in protein sequence analysis is comparing sequences to look for similarities. We propose an information theoretical distance to compare cellular automata representing protein sequences, and determine similarities. Our approach relies in a stationary Hamming distance for the evolution of the automata according to a properly chosen rule, and to build a pairwise similarity matrix and determine common ancestors among different species in a simpler and less computationally demanding computer codes when compared to other methods.
    MeSH term(s) Algorithms ; Cellular Automata ; Proteins
    Chemical Substances Proteins
    Language English
    Publishing date 2023-10-05
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0287880
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: New distance measure for comparing protein using cellular automata image.

    Luryane F Souza / Hernane B de B Pereira / Tarcisio M da Rocha Filho / Bruna A S Machado / Marcelo A Moret

    PLoS ONE, Vol 18, Iss 10, p e

    2023  Volume 0287880

    Abstract: One of the first steps in protein sequence analysis is comparing sequences to look for similarities. We propose an information theoretical distance to compare cellular automata representing protein sequences, and determine similarities. Our approach ... ...

    Abstract One of the first steps in protein sequence analysis is comparing sequences to look for similarities. We propose an information theoretical distance to compare cellular automata representing protein sequences, and determine similarities. Our approach relies in a stationary Hamming distance for the evolution of the automata according to a properly chosen rule, and to build a pairwise similarity matrix and determine common ancestors among different species in a simpler and less computationally demanding computer codes when compared to other methods.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

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