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MSClustering: A Cytoscape Tool for Multi-Level Clustering of Biological Networks

MSClustering is an efficient software package for visualizing and analyzing complex networks in Cytoscape. Based on the distance matrix of a network that it takes as input, MSClustering automatically displays the minimum span clustering (MSC) of the network at various characteristic levels. To produ...

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Detalles Bibliográficos
Autores principales: Ge, Bo-Kai, Hu, Geng-Ming, Chen, Rex, Chen, Chi-Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699063/
https://www.ncbi.nlm.nih.gov/pubmed/36430723
http://dx.doi.org/10.3390/ijms232214240
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author Ge, Bo-Kai
Hu, Geng-Ming
Chen, Rex
Chen, Chi-Ming
author_facet Ge, Bo-Kai
Hu, Geng-Ming
Chen, Rex
Chen, Chi-Ming
author_sort Ge, Bo-Kai
collection PubMed
description MSClustering is an efficient software package for visualizing and analyzing complex networks in Cytoscape. Based on the distance matrix of a network that it takes as input, MSClustering automatically displays the minimum span clustering (MSC) of the network at various characteristic levels. To produce a view of the overall network structure, the app then organizes the multi-level results into an MSC tree. Here, we demonstrate the package’s phylogenetic applications in studying the evolutionary relationships of complex systems, including 63 beta coronaviruses and 197 GPCRs. The validity of MSClustering for large systems has been verified by its clustering of 3481 enzymes. Through an experimental comparison, we show that MSClustering outperforms five different state-of-the-art methods in the efficiency and reliability of their clustering.
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spelling pubmed-96990632022-11-26 MSClustering: A Cytoscape Tool for Multi-Level Clustering of Biological Networks Ge, Bo-Kai Hu, Geng-Ming Chen, Rex Chen, Chi-Ming Int J Mol Sci Article MSClustering is an efficient software package for visualizing and analyzing complex networks in Cytoscape. Based on the distance matrix of a network that it takes as input, MSClustering automatically displays the minimum span clustering (MSC) of the network at various characteristic levels. To produce a view of the overall network structure, the app then organizes the multi-level results into an MSC tree. Here, we demonstrate the package’s phylogenetic applications in studying the evolutionary relationships of complex systems, including 63 beta coronaviruses and 197 GPCRs. The validity of MSClustering for large systems has been verified by its clustering of 3481 enzymes. Through an experimental comparison, we show that MSClustering outperforms five different state-of-the-art methods in the efficiency and reliability of their clustering. MDPI 2022-11-17 /pmc/articles/PMC9699063/ /pubmed/36430723 http://dx.doi.org/10.3390/ijms232214240 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
Ge, Bo-Kai
Hu, Geng-Ming
Chen, Rex
Chen, Chi-Ming
MSClustering: A Cytoscape Tool for Multi-Level Clustering of Biological Networks
title MSClustering: A Cytoscape Tool for Multi-Level Clustering of Biological Networks
title_full MSClustering: A Cytoscape Tool for Multi-Level Clustering of Biological Networks
title_fullStr MSClustering: A Cytoscape Tool for Multi-Level Clustering of Biological Networks
title_full_unstemmed MSClustering: A Cytoscape Tool for Multi-Level Clustering of Biological Networks
title_short MSClustering: A Cytoscape Tool for Multi-Level Clustering of Biological Networks
title_sort msclustering: a cytoscape tool for multi-level clustering of biological networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9699063/
https://www.ncbi.nlm.nih.gov/pubmed/36430723
http://dx.doi.org/10.3390/ijms232214240
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