document: Simple and effective visual models for gene expression cancer diagnostics In the paper we show that diagnostic classes in cancer gene expression data sets , which most often include thousands of features ( genes ) , may be effectively separated with simple two-dimensional plots such as scatterplot and radviz graph . The principal innovation proposed in the paper is a method called VizRank , which is able to score and identify the best among possibly millions of candidate projections for visualizations . Compared to recently much applied techniques in the field of cancer genomics that include neural networks , support vector machines and various ensemble-based approaches , VizRank is fast and finds visualization models that can be easily examined and interpreted by domain experts . Our experiments on a number of gene expression data sets show that VizRank was always able to find data visualizations with a small number of ( two to seven ) genes and excellent class separation . In addition to providing grounds for gene expression cancer diagnosis , VizRank and its visualizations also identify small sets of relevant genes , uncover interesting gene interactions and point to outliers and potential misclassifications in cancer data sets .
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