Neural network based virtual reality spaces for visual data mining of cancer data: an unsupervised perspective

Authors:
Enrique Romero;Julio J. Valdés;Alan J. Barton
Affiliations:
Dept. of Languages and Information Systems, Polytechnic University of Catalonia, Barcelona, Spain;National Research Council Canada, Ottawa, ON, Canada;National Research Council Canada, Ottawa, ON, Canada
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

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Abstract

Unsupervised neural networks are used for constructing virtual reality spaces for visual data mining of gene expression cancer data. Datasets representative of three of the most important types of cancer considered in modern medicine (liver, lung and stomach) are considered in the study. They are composed of samples from normal and tumor tissues, described in terms of tens of thousands of variables, which are the corresponding gene expression intensities measured in microarray experiments. Despite the very high dimensionality of the studied patterns, high quality visual representations in the form of structure-preserving virtual spaces are obtained using SAMANN neural networks, which enables the differentiation of cancerous and noncancerous tissues. The same networks could be used as nonlinear feature generators in a preprocessing step for other data mining procedures.