Class prediction and gene selection for DNA microarrays using sliced inverse regression

摘要

The monitoring of the expression profiles of thousands of genes seems particularly promising for biological classification. DNA microarrays data have been recently used for the development of classification rules, particularly for cancer diagnosis. However, microarrays data present major challenges due to the complex, multiclass nature and the overwhelming number of variables characterizing gene expression profiles. We propose an approach based on sliced inverse regression which allows the simultaneous development of classification rules and the selection of those genes that are most important in terms of classification accuracy.