The large number of features that can be measured simultaneously with microarray technology also presents a challenge. On one hand, it is likely that a large number of irrelevant features will be present; on the other hand, the scientist would like to work with a small number of strong, relevant features that can be used for diagnostic/prognostic panels, or as the basis for further biochemical validation studies of the mechanisms involved. This problem of feature selection also arises due to a fundamental limitation in statistics, sometimes called the ''curse of dimensionality'', according to which the existence of a large number of features necessitates an even larger (exponentially larger) number of samples to achieve consistent and accurate results. As the number of patients in microarray-based studies is severely restricted by factors such as the cost of the technology and difficulties in patient enrollment, it is almost always the case that only a small number of samples are available. Thus, only a small number of features at a time can be considered. The recommended approach to feature selection is to consider combinations of m features at a time, do classifier design based on the feature set under consideration, and use an estimate of its probability of error as the performance score. Application of this type of analysis to immunomic microarray data would allow the identification of sets of epitope-specific immune responses, associated for example with distinct disease states. However, feature selection presents an explosive combinatorial problem. For example, in an exhaustive selection of sets of three features among 1,000 initial features, the total number of feature sets to be assessed is equal to 166,167,000. If an initial set of 10,000 features is used instead, the number of feature sets of size three to be searched is larger than 10 11 . The complexity of feature selection is especially crucial in functional immunomics applications, since in this case the number of initial features to be considered is huge. The use of high-performance computing architectures, such as large computer clusters, is almost mandatory.