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Advances in Bioinformatics
Volume 2017 (2017), Article ID 1278932, 8 pages
https://doi.org/10.1155/2017/1278932
Review Article

Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis

1Department of Energy, Joint Genome Institute, Lawrence Berkeley Labs, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA
2Division of Basic Sciences, University of Crete Medical School, Andrea Kalokerinou Street, Heraklion, Greece

Correspondence should be addressed to Georgios A. Pavlopoulos; moc.liamg@soluopolvap.g and Ioannis Iliopoulos; rg.cou.dem@jpoili

Received 22 February 2017; Revised 14 May 2017; Accepted 4 June 2017; Published 18 July 2017

Academic Editor: Klaus Jung

Copyright © 2017 Georgios A. Pavlopoulos et al. 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.

Abstract

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.