Enabling dynamic network analysis through visualization in TVNViewer.
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.
Joint Carnegie Mellon, University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA.
Ross E
RE
Curtis
Jing
J
Xiang
Ankur
A
Parikh
Peter
P
Kinnaird
Eric P
EP
Xing
D001943:Breast Neoplasms / Q000235:genetics; D002453:Cell Cycle / Q000235:genetics; D003196:Computer Graphics; D005260:Female; D053263:Gene Regulatory Networks; D006801:Humans; D012441:Saccharomyces cerevisiae / Q000235:genetics; D012984:Software