Epistasis Blog

From the Computational Genetics Laboratory at the University of Pennsylvania (www.epistasis.org)

Sunday, September 13, 2015

The role of visualization and 3-D printing in biological data mining

Visualization and visual analytics are the future of informatics. We explore here the role of visualization and 3-D printing in biological data mining with application to statistical epistasis networks.

Weiss TL, Zieselman A, Hill DP, Diamond SG, Shen L, Saykin AJ, Moore JH; Alzheimer’s Disease Neuroimaging Initiative. The role of visualization and 3-D printing in biological data mining. BioData Min. 2015 Aug 5;8:22. [PDF]

Abstract

Biological data mining is a powerful tool that can provide a wealth of information about patterns of genetic and genomic biomarkers of health and disease. A potential disadvantage of data mining is volume and complexity of the results that can often be overwhelming. It is our working hypothesis that visualization methods can greatly enhance our ability to make sense of data mining results. More specifically, we propose that 3-D printing has an important role to play as a visualization technology in biological data mining. We provide here a brief review of 3-D printing along with a case study to illustrate how it might be used in a research setting.

We present as a case study a genetic interaction network associated with grey matter density, an endophenotype for late onset Alzheimer's disease, as a physical model constructed with a 3-D printer. The synergy or interaction effects of multiple genetic variants were represented through a color gradient of the physical connections between nodes. The digital gene-gene interaction network was then 3-D printed to generate a physical network model.

The physical 3-D gene-gene interaction network provided an easily manipulated, intuitive and creative way to visualize the synergistic relationships between the genetic variants and grey matter density in patients with late onset Alzheimer's disease. We discuss the advantages and disadvantages of this novel method of biological data mining visualization.




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