Cross model validated feature selection based on gene clusters

Discriminant partial least squares regression is used for classifying two leukaemia subtypes. Clusters from a hierarchical clustering are tested for significance by jack-knife. Cross model validation is used both to detect the optimal partitioning of genes and to validate the feature selection. A predictive model based on 24 out of 500 initial clusters proved to outperform a model based on single genes for the presented data. Some of the selected clusters were shown to be biologically meaningful, others may give clues to functional relationships.