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A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks

Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualizati...

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Autores principales: Hruz, Tomas, Wyss, Markus, Lucas, Christoph, Laule, Oliver, von Rohr, Peter, Zimmermann, Philip, Bleuler, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3707208/
https://www.ncbi.nlm.nih.gov/pubmed/23864855
http://dx.doi.org/10.1155/2013/920325
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author Hruz, Tomas
Wyss, Markus
Lucas, Christoph
Laule, Oliver
von Rohr, Peter
Zimmermann, Philip
Bleuler, Stefan
author_facet Hruz, Tomas
Wyss, Markus
Lucas, Christoph
Laule, Oliver
von Rohr, Peter
Zimmermann, Philip
Bleuler, Stefan
author_sort Hruz, Tomas
collection PubMed
description Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualization algorithms can lead to high running times and poorly readable visualizations due to many edge crossings. We suggest an approach that analyzes the structure of the graph first and then generates a new graph which contains specific semantic symbols for regular substructures like dense clusters. We propose a multilevel gamma-clustering layout visualization algorithm (MLGA) which proceeds in three subsequent steps: (i) a multilevel γ-clustering is used to identify the structure of the underlying network, (ii) the network is transformed to a tree, and (iii) finally, the resulting tree which shows the network structure is drawn using a variation of a force-directed algorithm. The algorithm has a potential to visualize very large networks because it uses modern clustering heuristics which are optimized for large graphs. Moreover, most of the edges are removed from the visual representation which allows keeping the overview over complex graphs with dense subgraphs.
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spelling pubmed-37072082013-07-17 A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks Hruz, Tomas Wyss, Markus Lucas, Christoph Laule, Oliver von Rohr, Peter Zimmermann, Philip Bleuler, Stefan Adv Bioinformatics Research Article Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualization algorithms can lead to high running times and poorly readable visualizations due to many edge crossings. We suggest an approach that analyzes the structure of the graph first and then generates a new graph which contains specific semantic symbols for regular substructures like dense clusters. We propose a multilevel gamma-clustering layout visualization algorithm (MLGA) which proceeds in three subsequent steps: (i) a multilevel γ-clustering is used to identify the structure of the underlying network, (ii) the network is transformed to a tree, and (iii) finally, the resulting tree which shows the network structure is drawn using a variation of a force-directed algorithm. The algorithm has a potential to visualize very large networks because it uses modern clustering heuristics which are optimized for large graphs. Moreover, most of the edges are removed from the visual representation which allows keeping the overview over complex graphs with dense subgraphs. Hindawi Publishing Corporation 2013 2013-06-26 /pmc/articles/PMC3707208/ /pubmed/23864855 http://dx.doi.org/10.1155/2013/920325 Text en Copyright © 2013 Tomas Hruz et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Hruz, Tomas
Wyss, Markus
Lucas, Christoph
Laule, Oliver
von Rohr, Peter
Zimmermann, Philip
Bleuler, Stefan
A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks
title A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks
title_full A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks
title_fullStr A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks
title_full_unstemmed A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks
title_short A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks
title_sort multilevel gamma-clustering layout algorithm for visualization of biological networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3707208/
https://www.ncbi.nlm.nih.gov/pubmed/23864855
http://dx.doi.org/10.1155/2013/920325
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