Efforts aimed at reducing noise, data overload in microarrays.

Microarrays have helped researchers identify previously unrecognized sub-types of cancers, and more recently they have been put to the test to determine their ability to identify cancers with better or worse prognosis (see News, Vol. 97, No. 5, p. 331 , " Trial and Error: Prog-nostic Gene Signature Study Design Altered "). Now, researchers are working to fi nd the best way to take the tool to a new level of complexity by asking it to help them identify genes involved in the basic biology of tumors. Experts in the fi eld expect that the approach will work — but caution that it won't be entirely straightforward. " For me, prediction is something we can often do without understanding the underlying biology , and that is much more diffi cult, " said Jill Mesirov, Ph.D., director of computational biology and bioinformatics at the Broad Institute at the Massachusetts Institute of Technology and Harvard in Cambridge, Mass. The problem boils down to issues of noise in the data and the ability to demonstrate biological relevance. For Mesirov, the use of gene sets, which are sometimes referred to as metagenes, can help address both problems. If, instead of analyzing the data in terms of individual genes, an investigator looks for gene sets that are enriched in a given tumor type, the data are likely to be more reproducible because the signal-to-noise ratio improves when 400 gene sets are analyzed versus 10,000 genes. Thus, genes that wouldn't show up very well individually may do so if they