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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9689808 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT lehotaykerypeter membraneclusteringofcoronavirusvariantsusingdocumentsimilarity AT kissattila membraneclusteringofcoronavirusvariantsusingdocumentsimilarity |