Analyzing gene expression data for childhood medulloblastoma survival with artificial neural networks

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 linking genes to the class values of samples. This results in a small but informative gene set for the domain of brain cancer that can be further analyzed through the application of symbolic machine learning techniques (See5) and cluster analysis (cluster and tree view).