Impact of experimental group, disease, mutation, age and sex on lymphoblast metabolic profiles.

Mutual Information (Information Gain) evidenced the features with more discriminative power between experimental conditions and enabled the selection of the most discriminant features for subsequent principal component analysis (PCA). We further evaluated the performance of a Naïve Bayes classifier with leave-one-out cross-validation using the selected features. Confusion matrices represent the number of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) in percent of the total real instances for each class. The following metrics were calculated: Area Under the Curve (AUC); Classification Accuracy (CA)=(TP + TN)/(TP + TN + FP + FN); F1 score = 2 x ((Precision x Recall)/(Precision + Recall)); Precision = TP/(TP + FP); Recall = TP/(TP + FN).