One of the major problems facing gene expression researchers is how to reduce the high dimensionality of gene expression data in the face of small sample sizes in comparison to the number of genes measured. We report on the application of a single layer neural network for reducing the number of originally measured genes from over 7000 to 64 through repeated application of thresholds on the weights...Show More
