Title |
Visual analysis of biological data-knowledge networks
|
---|---|
Published in |
BMC Bioinformatics, April 2015
|
DOI | 10.1186/s12859-015-0550-z |
Pubmed ID | |
Authors |
Corinna Vehlow, David P Kao, Michael R Bristow, Lawrence E Hunter, Daniel Weiskopf, Carsten Görg |
Abstract |
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. 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 an Cytoscape app. 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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 33% |
Norway | 1 | 11% |
Unknown | 5 | 56% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 5 | 56% |
Members of the public | 4 | 44% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 2% |
Brazil | 1 | 1% |
Turkey | 1 | 1% |
Canada | 1 | 1% |
Belgium | 1 | 1% |
Spain | 1 | 1% |
United States | 1 | 1% |
Luxembourg | 1 | 1% |
Unknown | 77 | 90% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 19 | 22% |
Student > Ph. D. Student | 16 | 19% |
Student > Master | 10 | 12% |
Student > Bachelor | 8 | 9% |
Other | 7 | 8% |
Other | 19 | 22% |
Unknown | 7 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 19 | 22% |
Computer Science | 17 | 20% |
Engineering | 11 | 13% |
Medicine and Dentistry | 11 | 13% |
Biochemistry, Genetics and Molecular Biology | 6 | 7% |
Other | 10 | 12% |
Unknown | 12 | 14% |