The identification of the change of gene expression in multifactorial diseases, such as breast cancer is a major goal of DNA microarray experiments. Here we present a new data mining strategy to better analyze the marginal difference in gene expression between microarray samples. The idea is based on the notion that the consideration of genes behavior in a wide variety of experiments can improve the statistical reliability on identifying genes with moderate changes between samples.