. 2015 May;43(5):725–734. doi: 10.1124/dmd.114.062539

TABLE 2.

Characteristics of Bayesian Models for MRP4 and BSEP Inhibition

Bayesian models	MRP4inhib-ECFP_6	MRP4inhib-FCFP_6	BSEPinhib-ECFP_6	BSEPinhib-FCFP_6
Two-dimensional fingerprints	ECFP_6	FCFP_6	ECFP_6	FCFP_6
10-fold XV ROC AUC^a	0.816	0.793	0.750	0.759
TP/FN/FP/TN^a	33/1/1/22	33/1/1/22	43/0/3/125	43/0/5/123
External validation^b	0.819	0.838	0.845	0.871
TP/FN/FP/TN^b	8/9/1/11	10/7/2/10	18/4/15/49	17/5/10/54
SE (%)^b	47.1	58.8	81.8	77.3
SP (%)^b	91.7	83.3	76.7	84.4
Q (%)^b	65.5	69.0	77.9	82.6
MCC^b	0.4123	0.4216	0.5238	0.5796

FN, false negative; FP, false positive; Q, overall prediction accuracy; SE, sensitivity; SP, specificity; TN; true negative; TP, true positive.

^{^a}

XV ROC AUC based on training set compounds (green shaded region).

^{^b}

Predictive performance validation by test set compounds (blue shaded region) (Ung et al., 2007; Khandelwal et al., 2008). SE = TP/(TP + FN); SP = TN/(TN + FP); Q = (TP + TN)/(TP + TN + FP + FN); MCC = [(TP * TN) – (FN * FP)]/[(TP + FP)(TP + FN)(TN +FN)(TN+FP)]^1/2.