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Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis

Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them. While many tools to manipulate, visu...

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Detalles Bibliográficos
Autores principales: Pavlopoulos, Georgios A., Paez-Espino, David, Kyrpides, Nikos C., Iliopoulos, Ioannis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5540468/
https://www.ncbi.nlm.nih.gov/pubmed/28804499
http://dx.doi.org/10.1155/2017/1278932
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author Pavlopoulos, Georgios A.
Paez-Espino, David
Kyrpides, Nikos C.
Iliopoulos, Ioannis
author_facet Pavlopoulos, Georgios A.
Paez-Espino, David
Kyrpides, Nikos C.
Iliopoulos, Ioannis
author_sort Pavlopoulos, Georgios A.
collection PubMed
description Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them. While many tools to manipulate, visualize, and interactively explore such networks already exist, only few of them can scale up and follow today's indisputable information growth. In this review, we shortly list a catalog of available network visualization tools and, from a user-experience point of view, we identify four candidate tools suitable for larger-scale network analysis, visualization, and exploration. We comment on their strengths and their weaknesses and empirically discuss their scalability, user friendliness, and postvisualization capabilities.
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spelling pubmed-55404682017-08-13 Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis Pavlopoulos, Georgios A. Paez-Espino, David Kyrpides, Nikos C. Iliopoulos, Ioannis Adv Bioinformatics Review Article Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them. While many tools to manipulate, visualize, and interactively explore such networks already exist, only few of them can scale up and follow today's indisputable information growth. In this review, we shortly list a catalog of available network visualization tools and, from a user-experience point of view, we identify four candidate tools suitable for larger-scale network analysis, visualization, and exploration. We comment on their strengths and their weaknesses and empirically discuss their scalability, user friendliness, and postvisualization capabilities. Hindawi 2017 2017-07-18 /pmc/articles/PMC5540468/ /pubmed/28804499 http://dx.doi.org/10.1155/2017/1278932 Text en Copyright © 2017 Georgios A. Pavlopoulos et al. https://creativecommons.org/licenses/by/4.0/ 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 Review Article
Pavlopoulos, Georgios A.
Paez-Espino, David
Kyrpides, Nikos C.
Iliopoulos, Ioannis
Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis
title Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis
title_full Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis
title_fullStr Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis
title_full_unstemmed Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis
title_short Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis
title_sort empirical comparison of visualization tools for larger-scale network analysis
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5540468/
https://www.ncbi.nlm.nih.gov/pubmed/28804499
http://dx.doi.org/10.1155/2017/1278932
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