Visual analysis of biological data-knowledge networks.
BACKGROUND
The interpretation of the results from genome-scale experiments is a challenging and important problem in contemporary biomedical research. Biological networks that integrate experimental results with existing knowledge from biomedical databases and published literature can provide a rich resource and powerful basis for hypothesizing about mechanistic explanations for observed gene-phenotype relationships. However, the size and density of such networks often impede their efficient exploration and understanding.


RESULTS
We introduce a visual analytics approach that integrates interactive filtering of dense networks based on degree-of-interest functions with attribute-based layouts of the resulting subnetworks. The comparison of multiple subnetworks representing different analysis facets is facilitated through an interactive super-network that integrates brushing-and-linking techniques for highlighting components across networks. An implementation is freely available as a Cytoscape app.


CONCLUSIONS
We demonstrate the utility of our approach through two case studies using a dataset that combines clinical data with high-throughput data for studying the effect of β-blocker treatment on heart failure patients. Furthermore, we discuss our team-based iterative design and development process as well as the limitations and generalizability of our approach.
VISUS, University of Stuttgart, Allmandring 19, Stuttgart, Germany. corinna.vehlow@visus.uni-stuttgart.de.
Corinna
C
Vehlow
School of Medicine, University of Colorado, E 17th Pl, Aurora, CO, USA. david.kao@ucdenver.edu.
David P
DP
Kao
School of Medicine, University of Colorado, E 17th Pl, Aurora, CO, USA. michael.bristow@ucdenver.edu.
Michael R
MR
Bristow
School of Medicine, University of Colorado, E 17th Pl, Aurora, CO, USA. larry.hunter@ucdenver.edu.
Lawrence E
LE
Hunter
VISUS, University of Stuttgart, Allmandring 19, Stuttgart, Germany. daniel.weiskopf@visus.uni-stuttgart.de.
Daniel
D
Weiskopf
School of Medicine, University of Colorado, E 17th Pl, Aurora, CO, USA. carsten.goerg@ucdenver.edu.
Carsten
C
Görg
D000319:Adrenergic beta-Antagonists / Q000494:pharmacology*; D002784:Cholesterol / Q000378:metabolism*; D053480:Cholesterol Ester Transfer Proteins / Q000235:genetics / Q000378:metabolism*; D003196:Computer Graphics; D057225:Data Mining; D016208:Databases, Factual; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006333:Heart Failure / Q000188:drug therapy / Q000235:genetics*; D006801:Humans; D012984:Software
D000319:Adrenergic beta-Antagonists; C506642:CETP protein, human; D053480:Cholesterol Ester Transfer Proteins; D002784:Cholesterol