Millliclass microarray data classification using GA/ANN method

Abstract

This work aims to explore the use of gene expression data in discriminating heterogeneous cancers. We introduce hybrid learning methodology that integrates genetic algorithms (GA) and artificial neural networks (ANN) to find optimal subsets of genes for tissue/cancer classification. This method was tested on two published microarray datasets: (1) NCI60 cancer cell lines and (2) the GCM dataset. Experimental results on classifying both datasets show that our GA/ANN method not only outperformed many reported prediction approaches, but also reduced the number of predictive genes needed in classification analysis. © Springer-Verlag Berlin Heidelberg 2006.