Supervised analysis when the number of candidate features (p) greatly exceeds the number of cases (n)

New genomic and proteomic technologies provide measurements of thousands of features for each case. This provides a context for enhanced discovery and false discovery. Most statistical and machine learning procedures were not developed for the p>>n setting and the literature of DNA microarray studies contains many examples of mis-use of analytic and computatinal methods such a cross-validation. This paper highlights some of key aspects of p>>n problems for identifying informative features and developing accurate classifiers.