Hybrid feature selection method for gene expression analysis

Feature selection is important and necessary for disease classification and prediction using high-dimensional gene expression data. A hybrid method integrating sparse representation with a two-sample statistical t-test to construct features from high-throughput microarray data is presented. The approach takes account of gene interaction and reduces the variable dimension by sparse linear combination, as well as considers the discriminative power of genes using component regression. Under the recurrent independence rule for classification, the experiment results on real data demonstrate the improvements of this hybrid technique over conventional methods.