Neural networks for gene expression analysis and gene selection from DNA microarray

We propose two approaches for microarray gene expression analysis and gene selection using neural networks. Using these approaches, only those genes which help sample classification are selected from the original set of genes, and the redundant genes expression patterns involved in the huge microarray matrix are eliminated so that dimensionality of the matrix is reduced from a few thousands to a much smaller number. An unsupervised SOM based technique and another supervised single layer perceptron based technique have been utilized for this purpose. Performance of these two approaches is compared in terms of accuracy, implementation and execution time.