This page contains supplemental material for the following paper submitted to Bioinformatics:

VizRank: Finding Informative Data Projections in Functional Genomics by Machine Learning
Gregor Leban, Ivan Bratko, Uros Petrovic, Tomaz Curk and Blaz Zupan

Abstract: VizRank is a tool that finds interesting two-dimensional projections of class-labeled data. When applied to multi-dimensional functional genomics data sets, VizRank can systematically find relevant biological patterns.

Supplemental information is available for the following topics:

• Installation, where you can download Orange, the data mining package that includes our implementation of VizRank for scatterplot and radviz visualizations.
• Start with VizRank in Orange, where we show how to load the data and rank two-dimensional projections by VizRank in Orange.
• VizRank's graphical interface, where we show and describe VizRank's user interface.
• VizRank method details, where we provide detailed description of the method for ranking of two-dimensional data projections.
• Supported visualization methods, where we describe the visualization methods which are associated with VizRank in our implementation.
• Comparison with other methods, where we shortly describe other visualization methods used by genetics and compare them to VizRank.
• S. cerevisiae metabolic example, for which we show insightful projections that separate between three functional groups (respiration, cytoplasmic ribosomes and proteasome) from the data from study of Brown et al. (2000).
• S. cerevisiae cell-cycle example, where we present interesting data projections found by VizRank. The data used in this example is from the study by Spellman et al. (1998).

References:

Brown, M.P., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C., Furey, T.S., Ares, M., Haussler, D. (2000) Knowledge-based analysis of microarray gene expression data by using support vector machines, Proceedings of the National Academy of Sciences, 1 , 262�267.

Demsar, J., Zupan, B. (2004) Orange : From Experimental Machine Learning to Interactive Data Mining, A White Paper. AI Lab, Faculty of Computer and Information Science, Ljubljana.

DeRisi J, Iyer V, Brown P. (1997) Exploring the metabolic and genetic control of gene expression on a genomic scale. Science, 278 , 680--6.

McCarthy, J.F., Marx, K.A., Hoffman, P.E., Gee, A.G., O'Neil, P., Ujwal, M.L., Hotchkiss J. (2004) Applications of Machine Learning and High-Dimensional Visualization in Cancer Detection, Diagnosis, and Management, Annals of the New York Academy of Sciences, 1020 , 239�262.

Hoffman, P.E., Grinstein, G., Marx, K., Grosse, I., Stanley, E. (1997) DNA Visual and Analytic Data Mining, IEEE Visualization 1997, 1 , 437�441.