Table 5.

Best ROC AUC and AUPR (precision-recall AUC) values obtained for good versus poor responder classifiers built using different ML methods without/with FloWPS for different MM annotated expression datasets.

Dataset	SVM	RF	RR	BNB	MLP
Current study full ROC AUC	0.80/0.82	0.76/0.82	0.86/0.87	0.79/0.84	0.81/0.83
Current study full AUPR	0.79/0.82	0.78/0.79	0.88/0.90	0.78/0.83	0.79/0.81
Current study VCD ROC AUC	0.82/0.86	0.74/0.83	0.86/0.87	0.78/0.88	0.84/0.89
Current study VCD AUPR	0.82/0.86	0.76/0.86	0.86/0.84	0.79/0.92	0.83/0.88
GSE9782 ROC AUC	0.68/0.72	0.68/0.80	0.77/0.77	0.73/0.76	0.72/0.76
GSE9782 AUPR	0.65/0.70	0.70/0.80	0.77/0.77	0.69/0.76	0.69/0.74
GSE68871 ROC AUC	0.68/0.77	0.73/0.83	0.78/0.77	0.74/0.84	0.70/0.80
GSE68871 AUPR	0.64/0.76	0.73/0.83	0.79/0.77	0.71/0.80	0.69/0.76
GSE55145 ROC AUC	0.78/0.82	0.77/0.90	0.87/0.84	0.82/0.87	0.80/0.85
GSE55145 AUPR	0.72/0.84	0.72/0.84	0.88/0.85	0.83/0.82	0.83/0.82
GSE19784_1 ROC AUC	0.65/0.82	0.74/0.77	0.84/0.84	0.74/0.84	0.72/0.81
GSE19784_1 AUPR	0.64/0.77	0.71/0.77	0.86/0.84	0.72/0.84	0.69/0.79
GSE19784_2 ROC AUC	0.83/0.87	0.75/0.82	0.92/0.94	0.88/0.94	0.86/0.87
GSE19784_2 AUPR	0.85/0.91	0.79/0.86	0.96/0.97	0.92/0.97	0.88/0.89
GSE19784_3 ROC AUC	0.84/0.94	0.84/0.86	0.95/0.95	0.86/0.96	0.91/0.94
GSE19784_3 AUPR	0.89/0.95	0.89/0.90	0.98/0.98	0.92/0.98	0.95/0.96
GSE2658 ROC AUC	0.72/0.77	0.67/0.79	0.79/0.79	0.76/0.78	0.63/0.72
GSE2658 AUPR	0.45/0.55	0.51/0.61	0.58/0.61	0.49/0.54	0.42/0.48