Microarray data analysis using neural network classifiers and gene selection methods

Different research groups have conducted independent gene expression studies on tissue samples from human lung adenocarcinomas [Bhattacharjee et al. 2001; Beer et al. 2002]. In this paper we (a) investigate methods to integrate data obtained from independent studies, (b) experiment with different gene selection methods to find genes that have significantly differential expression among different tumor stages, (c) study the performance of neural network classifiers with correlated weights, and (d) compare the performance of classifiers based on neural networks and its many variants on gene expression data. Raw cell intensity data were preprocessed for our analyses. Affymetrix array comparison spreadsheets were used to extract the overlapping probe sets for the data integration study. We considered neural network classifiers with random weights selected from a univariate normal distribution and optimized using Bayesian methods. The performance of the neural network was further enhanced using ensemble techniques such as bagging and boosting. The performance of all the resulting classifiers was compared using the Michigan and Harvard data sets from the CAMDA website. Three gene selection methods were used to find significant genes that could discriminate between the various stages of lung cancer. Significant genes, which were mined from the Gene Ontology (GO) database using the GoMiner and AmiGO packages, were found to be involved in apoptosis, angiogenesis, and cell growth and differentiation. Neural networks enhanced with bagging exhibited the best performance among all the classifiers we tested.