  Motivation: Microarray data has been recently been shown to be efficacious in
distinguishing closely related cell types that often appear in the diagnosis of
cancer. It is useful to determine the minimum number of genes needed to do such
a diagnosis both for clinical use and to determine the importance of specific
genes for cancer. Here a replication algorithm is used for this purpose. It
evolves an ensemble of predictors, all using different combinations of genes to
generate a set of optimal predictors.
  Results: We apply this method to the leukemia data of the Whitehead/MIT group
that attempts to differentially diagnose two kinds of leukemia, and also to
data of Khan et. al. to distinguish four different kinds of childhood cancers.
In the latter case we were able to reduce the number of genes needed from 96
down to 15, while at the same time being able to perfectly classify all of
their test data.
  Availability: http://stravinsky.ucsc.edu/josh/gesses/
  Contact<EMAIL_ADDRESS>