Gene selection in microarray data: the elephant, the blind men and our algorithms.
Gene expression array data provide shadows of intricate cellular processes. Learning how to make the most of the information present in expression arrays has become a discipline in itself. In recent years, there has been an explosion of methods that analyze gene expression arrays to produce long lists of genes that express differentially in distinct cellular states. These lists will have to be organized, and the algorithms that produced them combined, if we wish to piece together the rich cellular structures probed by this high-throughput technology. Researchers will have to understand the benefits and limitations of the many existing methods to produce the combination of algorithms that best suits their gene expression experiments.
Stolovitzky
Gustavo
G
IBM TJ Watson Research Center, PO Box 218, Yorktown Heights, NY 10598, USA. gustavo@us.ibm.com
D000465:Algorithms; D003627:Data Interpretation, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*