  Microarray data are often used to determine which genes are differentially
expressed between groups, for example, between treatment and control groups.
There are methods of determining which genes have a high probability of
differential expression, but those methods depend on the estimation of
probability densities. Theoretical results have shown such estimation to be
unreliable when high-probability genes are identified.
  The genes that are probably differentially expressed can be found using
decision theory instead of density estimation. Simulations show that the
proposed decision-theoretic method is much more reliable than a
density-estimation method. The proposed method is used to determine which genes
to consider differentially expressed between patients with different types of
cancer.
  The proposed method determines which genes have a high probability of
differential expression. It can be applied to data sets that have replicate
microarrays in each of two or more groups of patients or experiments.
