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Table 1 Characteristics of tools for identifying pre-miRNAs.

From: ViralmiR: a support-vector-machine-based method for predicting viral microRNA precursors

Tool Classifier Used features SN (%) SP (%) References
Triplet-SVM SVM Each hairpin is encoded as a set of 32 triplet elements 93.3 88.1 Xue et al. [9]
MiPred Random forest 32 Triplet-SVM features and a minimum of the free energy of the secondary structure 89.3 93.2 Jiang et al. [10]
miPred SVM 17 primary sequencing features, 5 secondary structural features, and 7 normalized features 84.5 97.9 Ng and Mishra [11]
miR-KDE RVKDE 29 miPred features and 4 stem-loop features 88.9 92.6 Chang et al. [12]
microPred SVM 29 miPred features, 4 RNAfold-related features, 6 Mfold-related features, 7 base-pair-related features, and 2 MFE-related features 83.3 99.0 Batuwita et al. [13]
MiRenSVM SVM 8 triplet structural features, 8 base-pair group features, 16 thermodynamic group features 87.7 98.8 Ding et al. [14]
miR-BAG Naïve Bayes
BF Tree
SVM
4 mononucleotide features, 16 dinucleotide features, 20 triplet structural features, consecutive paired bases, structural profile scoring, and normalized sequence-based total-pairing features 89.8 91.5 Ashwani Jha et al. [15]
  1. SN: sensitivity; SP: specificity