Patricio Alzamora
ABSTRACT
This paper describes a new three-dimensional interactive visualization supporting large scale medical data analysis. We provide a simple and effective view so that the biomedical information can be easily perceived. Our visualization also embeds a novel mechanism to prevent disorientation by maintaining the orientation of objects and labels during the navigation. From the overview of patient population, users can select one, multiple patients or a group of patients to analyse further. We demonstrate our approach with the medical scientists working on a case study of childhood cancer patients, examplifying how they could use the tool to confirm existing hypotheses and to discover new scientific insights.
- Barsky, A., Munzner, T., Gardy, J., and Kincaid, R. 2008. Cerebral: Visualizing Multiple Experimental Conditions on a Graph with Biological Context. IEEE Trans. Visualization and Computer Graphics (Proc. InfoVis 2008). 14, 6, pp. 1253--1260. Google Scholar
Digital Library
- Belkin M., Niyogi P. 2001. Laplacian Eigenmaps and spectral techniques for embedding and clustering. In Proceedings of Advances in Neural Information Processing Systems. MIT Press: Cambridge, MA, 585--591.Google Scholar
- Breiman, L, Radom Forests, Machine Learning, 45 (2001), pp. 5--32. Google ScholarDigital Library
- Catchpoole, D. R., Kennedy, P., Skillicorn, D. B. and Simoff, S. 2010. The curse of dimensionality: a blessing to personalized medicine. Journal of Clinical Oncology. 28, 34 (Dec. 2010), 723--724.Google ScholarCross Ref
- Chao, S., Lihui, C. 2005. Feature dimension reduction for microarray data analysis using locally linear embedding. In Proceedings of APBC, pp. 211--217.Google ScholarCross Ref
- Chen, C. 2005. Top 10 Unsolved Information Visualization Problems. IEEE Computer Graphics and Applications. 25, 4, pp. 12--16. Google ScholarDigital Library
- Chen, Y., Meltzer, P. S. 2005. Gene expression analysis via multidimensional scaling. Current Protocols in Bioinformatics, Chapter 7, unit 7.11.Google Scholar
- Gehlenborg, N., O'Donoghue, S., Baliga, N., Goesmann, A., Hibbs, M., Kitano, H., Kohlbacher, O., Neuweger, H., Schneider, R., Tenenbaum, D., and Gavin, A. 2010. Visualization of omics data for systems biology. Nature Methods, 7:S56--S68.Google ScholarCross Ref
- Golub, G. H. and Van Loan, C. F. 1996. Matrix Computations. Johns Hopkins University Press, Baltimore, MD, USA.Google Scholar
- Goronzy, J. J., Matteson, E. L., Fulbright, J. W. et al. (2004): Prognostic markers of radiographic progression in early rheumatoid arthritis. Arthritis & Rheumatism. 50, 1, pp. 43--54.Google ScholarCross Ref
- Hu, Z. et al. 2009. VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology. Nucleic Acids Res. 37 (web server issue), W115--W121.Google Scholar
- Jolliffe, I. T. 2002. Principle Component Analysis. Springer, New York, 2002.Google Scholar
- Junker, B. H., Klukas, C. & Schreiber, F. 2006. VANTED: a system for advanced data analysis and visualization in the context of biological networks. BMC Bioinformatics. 7, 109.Google ScholarCross Ref
- McGuffin, M. J. & Jurisica, I. 2009. Interaction techniques for selecting and manipulating subgraphs in network visualizations. IEEE Trans. Vis. Comput. Graph. 15, pp. 937--944. Google ScholarDigital Library
- McLachlan, G. J., Do, K. A., Ambroise, C. 2004. Analyzing Microarray Gene Expression Data. Wiley, Hoboken, N. J.Google Scholar
- McLachlan, G. J., Wang, K. and Ng, S. K. 2008. Large-scale simultaneous inference with applications to the detection of differential expression with microarray data (with discussion). Statistica. 68, pp. 1--30.Google Scholar
- Nguyen Q, Gleeson A, Ho N, Huang M, Simoff S, Catchpoole D, 2011, Visual Analytics of Clinical and Genetic Datasets of Acute Lymphoblastic Keukaemia, ICONIP 2011 - 18th International Conference, pp. 113--120. Google ScholarDigital Library
- Nguyen, Q. V., Simoff, S. and Huang, M. L., Interactive Visualization with User Perspective: A New Concept, in Proceedings of VINCI 2010 - The 3rd Visual Information Communication - International Symposium, Beijing, China, ACM (2010), 84--89. Google ScholarDigital Library
- Procter, J., Thompson, J., Letunic, I., Creevey, C., Jossinet, F., and Barton, G. J. 2010. Visualization of multiple alignments, phylogenies and gene family evolution. Nature Methods, 7, S16--25.Google ScholarCross Ref
- Qi, Q., Zhao, Y., Li., M. C., Simon, R. 2009. Non-negative matrix factorization of gene expression profiles: a plug-in for BRB-arraytools. Bioinformatics. 25, 4, pp. 545--547. Google ScholarDigital Library
- Thomas, J. and Kielman, J. "Challenges for Visual Analytics" (2009): Information Visualization, 8(4), pp. 309--314. Google ScholarDigital Library
Index Terms
- A novel 3D interactive visualization for medical data analysis
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