Although PCA is useful for visualizing data it cannot provide a classification rule for discriminating between patient categories. To find such discriminatory proteomic patterns, we adopted a supervised learning approach in which patient categories are used to train an algorithm to derive a classification rule. We used a Support Vector Machine (SVM) method [19] . Briefly, we used 10-fold cross validation to select parameters for the SVM. For the final model parameters, we selected those that gave the overall highest accuracy across the whole 10 fold cross validation. We next selected a subset of the most relevant mass clusters using the Recursive Feature Elimination (RFE) algorithm [20] which ranks variables based on their contribution to the classifier. To obtain accuracy estimates for the classifier, we took 1000 random resamplings of the original data, using 90% for training and 10% for testing. We selected as a final classifier the one that produced the highest accuracy while requiring the smallest number of m/z clusters. Results were expressed as sensitivity, specificity and accuracy (proportion of correct classifications) and as Receiver Operator Characteristic (ROC) curves. We assessed the different performances of classifiers derived from crude and pre-fractionated plasma by comparing mean values for sensitivity, specificity and accuracy using unpaired 2-tailed t tests. Comparisons of categorical data were by Fisher's exact test.