Carnegie Mellon University
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Enabling dynamic network analysis through visualization in TVNViewer.

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posted on 2012-01-01, 00:00 authored by Ross E. Curtis, Jing Xiang, Ankur P. Parikh, Peter Kinnaird, Eric P Xing

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), 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.

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© 2012 Curtis et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Date

2012-01-01

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    Keywords

    Breast NeoplasmsCell CycleComputer GraphicsFemaleGene Regulatory NetworksHumansSaccharomyces cerevisiaeSoftwareKnowledge Representation and Machine Learning

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    CC BY 3.0

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