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Enabling dynamic network analysis through visualization in TVNViewer

Ross E Curtis12, Jing Xiang3, Ankur Parikh3, Peter Kinnaird4 and Eric P Xing3*

Author Affiliations

1 Joint Carnegie Mellon, University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA

2 Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA

3 Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA

4 Human Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA, USA

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BMC Bioinformatics 2012, 13:204  doi:10.1186/1471-2105-13-204

Published: 16 August 2012

Abstract

Background

Many biological processes are context-dependent or temporally specific. As a result, relationships between molecular constituents evolve across time and environments. While cutting-edge machine learning techniques can recover these networks, exploring and interpreting the rewiring behavior is challenging. Information visualization shines in this type of exploratory analysis, motivating the development ofTVNViewer (http://sailing.cs.cmu.edu/tvnviewer webcite), a visualization tool for dynamic network analysis.

Results

In this paper, we demonstrate visualization techniques for dynamic network analysis by using TVNViewer to analyze yeast cell cycle and breast cancer progression datasets.

Conclusions

TVNViewer is a powerful new visualization tool for the analysis of biological networks that change across time or space.

Keywords:
Visualization; Dynamic network analysis; Gene expression analysis