Visual analysis of biological data-knowledge networks. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Vehlow, Corinna; Kao, David; Bristow, Michael; Hunter, Lawrence; Weiskopf, Daniel; Görg, Carsten
• Is Part Of: BMC bioinformatics. Volume 16:Issue 1(2015)
• Page Start: 1
• Page End: 15
• Publication Date: 2015-12
• Subjects: Network analysis -- Degree-of-interest functions -- Interactive visualization; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-015-0550-z
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract 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 ofβ -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.