Table 1

One-class results obtained from the secondary features plus sequence features.

C. elegans
 Mouse
 Human
 All-miRNA

Method
 Sen
 Spe
 MCC
 Sen
 Spe
 MCC
 Sen
 Spe
 MCC
 Sen
 Spe
 MCC
 Average MCC
OC-SVM
 0.73
 0.93
 0.67
 0.80
 0.93
 0.74
 0.72
 0.99
 0.74
 0.69
 0.91
 0.62
 0.70
OC-Gaussian
 0.84
 0.93
 0.77
 0.89
 0.93
 0.82
 0.82
 0.99
 0.82
 0.82
 0.99
 0.82
 0.81
OC-Kmeans
 0.79
 0.93
 0.73
 0.85
 0.92
 0.77
 0.89
 0.92
 0.81
 0.89
 0.80
 0.69
 0.75
OC-PCA
 0.87
 0.89
 0.76
 0.88
 0.92
 0.80
 0.90
 0.79
 0.69
 0.90
 0.86
 0.76
 0.77
OC-KNN
 0.90
 0.86
 0.76
 0.90
 0.92
 0.82
 0.90
 0.96
 0.86
 0.90
 0.93
 0.83
 0.82
Two-Class
Naïve Bayes
 0.89
 0.93
 0.82 (125)
 0.93
 0.97
 0.90 (200)
 0.99
 0.92
 0.92 (300)
 0.97
 0.96
 0.93 (4000)
 0.88
SVM
 0.90
 0.97
 0.87 (200)
 0.95
 0.98
 0.93 (500)
 0.99
 0.99
 0.98 (300)
 0.98
 0.95
 0.93 (900)
 0.92

Sen = sensitivity, Spe = specificity, and MCC = Matthews Correlation Coefficient. Results are presented for four genomes individually (C. elegans, Mouse, and Human) and All-miRNA as a mixture of multiple miRNAs species. The number in parentheses is the corresponding number of optimal negative examples giving the highest MCC.

Yousef et al. Algorithms for Molecular Biology 2008 3:2   doi:10.1186/1748-7188-3-2