Abstract
Supervised methods for analyzing microarray data are discussed;
signal-to-noise methods, support vector machines and multilayer
perceptrons
with principal component preprocessing. The methods are illustrated
with
clinical applications for diagnostics and gene extraction; small round
blue
cell tumors (SRBCT) of childhood and breast cancer. In the context
of
gene extraction, pathway "depths" are investigated. The importance
of
using
blind test sets for evaluation is stressed. Also, sample selection
issues
are emphasized. Advantages of merging information from different kinds
of arrays are discussed.