Table 2. Summary of Statistical Methods Used To Evaluate Predictive Methods.

quantification method	description
concordance CCa	The concordance correlation coefficient measures the degree to which the predicted ΔΔG value equals the actual experimental value (0 indicates no agreement and 1 perfect agreement).
Pearson CCa	The Pearson correlation coefficient measures the degree to which a uniform linear transformation of the predicted ΔΔG values (i.e., a shift and scale change) would yield the actual experimental values (0 indicates no agreement after transformation, 1 perfect agreement, and −1 perfect inverse agreement).
Spearman rank CCa	The Spearman rank correlation coefficient measures the degree to which the rank ordering of the predicted ΔΔG values matches the rank ordering of the actual experimental values (0 indicates no agreement after transformation, 1 perfect agreement, and −1 perfect inverse agreement).
ROC and AUC	The area-under-the-receiver operating characteristic (ROC) curve tests several cutoff values for binning mutations as neutral or destabilizing between the most negative calculated ΔΔG value and the most positive calculated ΔΔG value, with true positive rates (sensitivity) calculated at each point. As the true positive rate is calculated, the classifier is moved to less extreme values; this yields the ROC curve. The AUC curve is a summary statistic that approximates how well the predictor actually discriminates between the two classifications.

^aCC indicates correlation coefficient.