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Table 1 One-class results obtained from the secondary features plus sequence features.

From: Learning from positive examples when the negative class is undetermined- microRNA gene identification

  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
  1. 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.