We introduce a method of functionally classifying genes by using gene expre
ssion data from DNA microarray hybridization experiments. The method is bas
ed on the theory of support vector machines (SVMs). SVMs are considered a s
upervised computer learning method because they exploit prior knowledge of
gene function to identify unknown genes of similar function from expression
data. SVMs avoid several problems associated with unsupervised clustering
methods, such as hierarchical clustering and self-organizing maps. SVMs hav
e many mathematical features that make them attractive for gene expression
analysis, including their flexibility in choosing a similarity function, sp
arseness of solution when dealing with large data sets, the ability to hand
le large feature spaces, and the ability to identify outliers. We test seve
ral SVMs that use different similarity metrics, as well as some other super
vised learning methods, and find that the SVMs best identify sets of genes
with a common function using expression data. Finally, we use SVMs to predi
ct functional roles for uncharacterized yeast ORFs based on their expressio
n data.