Using Artificial Neural Networks to Perform Feature Selection on Microarray Data

Abstract

This article illustrates a feature selection technique that makes use of artificial neural networks. The problem being faced is the analysis of microarray expression data, which requires a mandatory feature selection step due to the strong imbalance between number of features and size of the training set. The proposed technique has been assessed on relevant benchmark datasets. All datasets report gene expression levels taken from female subjects suffering from breast cancer against normal subjects. Experimental results, with average accuracy of about 84% and very good balance between specificity and sensitivity, point to the validity of the approach.