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Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo
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Cerebral: visualizing multiple experimental conditions on a graph with biological context.

Aaron Barsky1, Tamara Munzner, Jennifer Gardy

  • 1Department of Computer Science, University of British Columbia. barskya@cs.ubc.ca

IEEE Transactions on Visualization and Computer Graphics
|November 8, 2008
PubMed
Summary
This summary is machine-generated.

Systems biologists can now analyze cellular reactions more effectively using Cerebral. This new system integrates experimental data directly into biological interaction graphs, improving data visualization and analysis for immunologists.

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Area of Science:

  • Systems biology
  • Immunology
  • Bioinformatics

Background:

  • Systems biologists model cellular behavior using interaction graphs.
  • These graphs are dynamic knowledge representations that evolve with experimental data.
  • Existing graph visualization tools do not fully meet the needs of immunologists.

Purpose of the Study:

  • To describe the data information display needs of immunologists.
  • To design a system that addresses these needs for analyzing biological interaction graphs.
  • To incorporate experimental data directly into graph displays for enhanced analysis.

Main Methods:

  • Developed Cerebral, a system with biologically guided graph layout.
  • Integrated experimental data directly into the graph display.
  • Implemented small multiples for different experimental conditions and a parallel coordinates view.

Main Results:

  • Cerebral allows simultaneous analysis of data within the graph context and across experimental conditions.
  • Coordinated views enable biologists to examine data from multiple perspectives.
  • Analysis of two datasets demonstrated the system's utility.

Conclusions:

  • Cerebral is a valuable tool for systems biologists, particularly immunologists.
  • The system enhances the analysis of experimental data within interaction graph models.
  • Biologically guided layout and integrated data display improve biological insight.