Using a genetic algorithm and a perceptron for feature selection and supervised class learning in DNA microarray data

Class prediction and feature selection is key in the context of diagnostic applications of DNA microarrays. Microarray data is noisy and typically composed of a low number of samples and a large number of genes. Perceptrons can constitute an efficient tool for accurate classification of microarray data. Nevertheless, the large input layers necessary for the direct application of perceptrons and the low samples available for the training process hamper its use. Two strategies can be taken for an optimal use of a perceptron with a favourable balance between samples for training and the size of the input layer: (a) reducing the dimensionality of the data set from thousands to no more than one hundred, highly informative average values, and using the weights of the perceptron for feature selection or (b) using a selection of only few genes that produce an optimal classification with the perceptron. In this case, feature selection is carried out first. Obviously, a combined approach is also possible. In this manuscript we explore and compare both alternatives. We study the informative contents of the data at different levels of compression with a very efficient clustering algorithm (Self Organizing Tree Algorithm). We show how a simple genetic algorithm selects a subset of gene expression values with 100% accuracy in the classification of samples with maximum efficiency. Finally, the importance of dimensionality reduction is discussed in light of its capacity for reducing noise and redundancies in microarray data.