2008
DOI: 10.1186/1752-0509-2-104
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Arena3D: visualization of biological networks in 3D

Abstract: BackgroundComplexity is a key problem when visualizing biological networks; as the number of entities increases, most graphical views become incomprehensible. Our goal is to enable many thousands of entities to be visualized meaningfully and with high performance.ResultsWe present a new visualization tool, Arena3D, which introduces a new concept of staggered layers in 3D space. Related data – such as proteins, chemicals, or pathways – can be grouped onto separate layers and arranged via layout algorithms, such… Show more

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Cited by 106 publications
(74 citation statements)
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References 32 publications
(34 reference statements)
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“…Among public network management tools that currently exist to visually explore and analyse biological networks (see review in [60]) such as Arena3D [61], GEPHI [62], igraph [63] and VisANT [64] (Table 6.2), Cytoscape [65], CellDesigner [66] and Copasi [67] are the most powerful and widely used. Cytoscape (449,030 downloads as on September 2012) is the most popular software for the visualization and analysis of interaction networks.…”
Section: Network Analysis Toolsmentioning
confidence: 99%
“…Among public network management tools that currently exist to visually explore and analyse biological networks (see review in [60]) such as Arena3D [61], GEPHI [62], igraph [63] and VisANT [64] (Table 6.2), Cytoscape [65], CellDesigner [66] and Copasi [67] are the most powerful and widely used. Cytoscape (449,030 downloads as on September 2012) is the most popular software for the visualization and analysis of interaction networks.…”
Section: Network Analysis Toolsmentioning
confidence: 99%
“…The most commonly used tool is certainly Cytoscape (6,7), but others such as VisANT (8), Osprey (9), BioLayout Express3D (10), Arena3D (11), and PATIKA (12) are also cited. In the commercial space, Pathway Studio is commonly used (13).…”
Section: Interactive Tools For Protein Systems Datamentioning
confidence: 99%
“…AP clustering has been used successfully for clustering microarray/gene expression data (Frey and Dueck, 2007;Kiddle et al, 2010;Leone et al, 2007;Liu et al, 2010;Tang et al, 2010), in structural biology (North et al, 2011;Pandit and Skolnick, 2010;Wang et al, 2010), biological network analysis (Pavlopoulos et al, 2008;Vasblom and Wodak, 2009;Woźniak et al, 2010) and sequence analysis (Yang et al, 2010).…”
Section: Introductionmentioning
confidence: 99%