Finding the location of binding sites in DNA is a difficult problem. Although the location of some binding sites have been experimentally identified, other parts of the genome may or may not contain binding sites. This poses problems with negative data in a trainable classifier. Here we show that using randomized negative data gives a large boost in classifier performance when compared to the original labeled data.

Abstract Summary: The MAGE-TAB format for microarray data representation and exchange has been proposed by the microarray community to replace the more complex MAGE-ML format. We present a suite of tools to support MAGE-TAB generation and validation, conversion between existing formats for data exchange, visualization of the experiment designs encoded by MAGE-TAB documents and the mining of such documents for semantic content. Availability: Software is available from http://tab2mage.sourceforge.net/ Contact: [email protected]