On Combining Multiple Microarray Studies for Improved Functional Classification by Whole-Dataset Feature Selection

As microarray technologies become routinely applied in genome laboratories for studying gene expression, it is not uncommon that experiments on identical or similar sets of genes are conducted by multiple laboratories for various functional studies of these genes. Much of such data are often available to researchers for their data analysis, either through collaborators or from online gene expression databases. It will be useful to combine data from di#erent microarray studies to improve the microarray data mining results