Abstract: The increasing availability and maturity of DNA microarray technology has led to an explosion of cancer related studies. In order to evaluate, integrate, and cross-validate the accumulating amount of publicly available microarray data, new methods are needed. Statistical methods have been applied to raw microarray data to identify intersections of multiple gene expression signatures from a diverse collection of data sets. However, this analysis is very stringent and time consuming. Here we propose a fast method to detect common patterns in gene expression data by mining published lists of microarray experiments