Table 1.

Evaluation of isoform-level circular RNA prediction based on the 5-fold cross-validation

Method	Accuracy	Precision	Sensitivity	Specificity	F1-score	MCC	AUC
SVM	0.7279 ± 0.0686	0.7479 ± 0.0970	0.8932 ± 0.1075	0.4526 ± 0.3413	0.8042 ± 0.0260	0.4031 ± 0.1784	0.6729 ± 0.1203
RF	0.7607 ± 0.0084	0.7764 ± 0.0123	0.8610 ± 0.0077	0.5982 ± 0.0075	0.8165 ± 0.0095	0.4804 ± 0.0115	0.7296 ± 0.0053
Att-CNN	0.7519 ± 0.0069	0.7739 ± 0.0257	0.8529 ± 0.0391	0.5872 ± 0.0527	0.8105 ± 0.0080	0.4612 ± 0.0165	0.7200 ± 0.0097
Att-RNN	0.7638 ± 0.0075	0.7773 ± 0.0161	0.8582 ± 0.0345	0.6171 ± 0.0505	0.8152 ± 0.0094	0.4960 ± 0.0134	0.7377 ± 0.0105
nRC	0.7557 ± 0.0115	0.7844 ± 0.0389	0.8410 ± 0.0597	0.6193 ± 0.0998	0.8094 ± 0.0118	0.4781 ± 0.0279	0.8280 ± 0.0091
PredcircRNA	0.6550 ± 0.0076	0.6977 ± 0.0137	0.5949 ± 0.0070	0.7202 ± 0.0120	0.6422 ± 0.0091	0.3169 ± 0.0164	0.5882 ± 0.0102
circDeep	0.8748 ± 0.0102	0.9393 ± 0.0134	0.8161 ± 0.0217	0.9407 ± 0.0138	0.8732 ± 0.0111	0.7584 ± 0.0186	0.7395 ± 0.0132
DeepCirCode	0.8997 ± 0.0039	0.9353 ± 0.0228	0.9021 ± 0.0248	0.8967 ± 0.0383	0.9179 ± 0.0040	0.7914 ± 0.0073	0.8994 ± 0.0077
JEDI	0.9878 ± 0.0007	0.9906 ± 0.0030	0.9906 ± 0.0032	0.9836 ± 0.0038	0.9904 ± 0.0009	0.9742 ± 0.0014	0.9872 ± 0.0009

Note: We report the mean and standard deviation for each metric.