Predicting cancer type with dimensionality-reduced gene expression micro-array data

Predictive modeling based on gene expression data is complicated by the high dimensionality (number of genes) of microarray data given the number of available samples. We investigate a method for reducing the dimensionality of the data using singular value decomposition.