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Membrane Clustering of Coronavirus Variants Using Document Similarity

Currently, as an effect of the COVID-19 pandemic, bioinformatics, genomics, and biological computations are gaining increased attention. Genomes of viruses can be represented by character strings based on their nucleobases. Document similarity metrics can be applied to these strings to measure their...

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Detalles Bibliográficos
Autores principales: Lehotay-Kéry, Péter, Kiss, Attila
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689808/
https://www.ncbi.nlm.nih.gov/pubmed/36360202
http://dx.doi.org/10.3390/genes13111966
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author Lehotay-Kéry, Péter
Kiss, Attila
author_facet Lehotay-Kéry, Péter
Kiss, Attila
author_sort Lehotay-Kéry, Péter
collection PubMed
description Currently, as an effect of the COVID-19 pandemic, bioinformatics, genomics, and biological computations are gaining increased attention. Genomes of viruses can be represented by character strings based on their nucleobases. Document similarity metrics can be applied to these strings to measure their similarities. Clustering algorithms can be applied to the results of their document similarities to cluster them. P systems or membrane systems are computation models inspired by the flow of information in the membrane cells. These can be used for various purposes, one of them being data clustering. This paper studies a novel and versatile clustering method for genomes and the utilization of such membrane clustering models using document similarity metrics, which is not yet a well-studied use of membrane clustering models.
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spelling pubmed-96898082022-11-25 Membrane Clustering of Coronavirus Variants Using Document Similarity Lehotay-Kéry, Péter Kiss, Attila Genes (Basel) Article Currently, as an effect of the COVID-19 pandemic, bioinformatics, genomics, and biological computations are gaining increased attention. Genomes of viruses can be represented by character strings based on their nucleobases. Document similarity metrics can be applied to these strings to measure their similarities. Clustering algorithms can be applied to the results of their document similarities to cluster them. P systems or membrane systems are computation models inspired by the flow of information in the membrane cells. These can be used for various purposes, one of them being data clustering. This paper studies a novel and versatile clustering method for genomes and the utilization of such membrane clustering models using document similarity metrics, which is not yet a well-studied use of membrane clustering models. MDPI 2022-10-28 /pmc/articles/PMC9689808/ /pubmed/36360202 http://dx.doi.org/10.3390/genes13111966 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lehotay-Kéry, Péter
Kiss, Attila
Membrane Clustering of Coronavirus Variants Using Document Similarity
title Membrane Clustering of Coronavirus Variants Using Document Similarity
title_full Membrane Clustering of Coronavirus Variants Using Document Similarity
title_fullStr Membrane Clustering of Coronavirus Variants Using Document Similarity
title_full_unstemmed Membrane Clustering of Coronavirus Variants Using Document Similarity
title_short Membrane Clustering of Coronavirus Variants Using Document Similarity
title_sort membrane clustering of coronavirus variants using document similarity
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689808/
https://www.ncbi.nlm.nih.gov/pubmed/36360202
http://dx.doi.org/10.3390/genes13111966
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