Learning models for colorectal cancer signature reconstruction and classification in patients with chronic inflammatory bowel disease

Table 2 Summary of patients’ classification predicted by random forests/support vector machines respectively. From left to right: Group of patients, amount of selected miRNA, percentage of success in true positive classification, sensitivity, specificity and their area under the curve

Methods	Nº miRNA	% True classification (95%CI)	Sensitivity	Specificity	AUC
All miRNA
Strategy 1	56	69 (62-75)/69 (62-75)	0.25/0.43	0.93/0.83	0.76/0.74
CD	9	87 (78-93)/86 (77-92)	0.70/0.73	0.96/0.93	0.89/0.92
UC	30	72% (63-80)/76 (67-83)	0.45/0.55	0.86/0.87	0.77/0.81
miRNAs selected by sPLS-DA
Strategy 1	11	69 (62-75)/68 (62-75)	0.36/0.36	0.87/0.86	0.72/0.74
CD	5	80 (70-88)/82 (67-86)	0.67/0.60	0.87/0.87	0.84/0.86
UC	8	73 (64-80)/81 (73-88)	0.48/0.57	0.86/0.93	0.73/0.81

AUC: Area under curve; CD: Crohn’s disease; UC: Ulcerative colitis.