Table 3.

Characteristics of mathematical approaches for data analysis.

Panel A
	Sensitivity	Specificity
MOCA	67%	96%
Random Forest	76%	88%
SVM	65%	90%

Panel B
miR	Mean	Threshold
126	74.17	221.88
618	13.3	26.29
31	428.02	811.93
222	35.63	60.88
16	70.91	144.76
486-3p	46.78	82.30
484	29.26	54.49
19b	66.34	135.98
191	5.17	9.23
19a	41.40	85.70
1274b	61.00	165.87

Panel C
	Sensitivity	Specificity
191 U 486-3p U 1274b U 16 U 484	67%	96%
191 U 486-3p U 1274b U 16	65%	96%
191 U 486-3p U 1274b	59%	96%
191 U 486-3p	57%	96%

Panel A shows the predictive value achieved from each of three distinct mathematical approaches. The statistical Sensitivity and Specificity of CCA diagnosis achieved using selected miR species using the MOCA algorithm, SVM and RF. Panel B displays 11 differentially expressed miRs selected for analysis of predictive value. The table displays the 11 miR species, the Mean expression across 96 samples for that miR, and the Threshold above which a sample would be classified as CCA by MOCA. In all cases, the threshold is significantly greater than the mean across all samples for the corresponding miR. Panel C displays markers comprising five, four, three, or two miR species and corresponding predictive value. The statistical Sensitivity and Specificity of CCA classification achieved by four, representative multi-miR markers. Multi-miR markers were combined using the union (U) Boolean set operation.