Figure 1

The flowchart for the classifier development and validation using three gene sets: (A) Top 50 p-value ranked genes; (B) Top 50 VIP genes; and (C) the unique VIP genes. Specifically, the data set is first randomly divided into two thirds of samples for training and the remainder for validation. Next, three sets of genes are generated solely based on the training set, and are subsequently used to develop Nearest-Centroid classifiers. Lastly, the classifiers are used to predict the validation samples and their respective prediction performance measured by accuracy (Acc), specificity (Spec), sensitivity (Sens), and Matthew's correlation coefficient (MCC) are calculated. The process is repeated 50 times and the averaged performance metrics are reported in Table 2.