Large expression studies with clinical outcome data are becoming available for analysis. An important goal is to identify genes or clusters of genes where expression is related to patient outcome. While clustering methods are useful data exploration tools, they do not directly allow one to relate the expression data to clinical outcome. Alternatively, methods which rank genes based on their univariate significance do not incorporate gene function or relationships to genes that have been previously identified. In addition, after sifting through potentially thousands of genes, summary estimates (e.g. regression coefficients or error rates) algorithms should address the potentially large bias introduced by gene selection.