Estimation of cross-hybridization signals using support vector regression

Microarray technology is a powerful biotechnology tool which allows researchers to simultaneously evaluate the expression of thousands of genes, if not the entire expressed genome, of an organism. Measures of gene expression are determined by the differential hybridization of labeled mRNA from experimental samples to DNA probes affixed to the array. The accuracy of these measurements is influenced by the binding specificity between the labeled samples and the probes. Evaluating the level of cross-hybridization is therefore critically important in obtaining accurate measures of gene expression. In this paper we present a support vector regression based predictor that utilizes the nucleotide content of the DNA probes as a means for estimating the level of cross-hybridization. Experimental results from three microarray data sets are presented. Our results indicate that we can identify genes when the measured fluorescent signal values are less than those predicted from cross-hybridization. In these cases we do not consider the genes to be expressed.