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Table 4 Performance of different WEKA methods for Dicer cleavage prediction and their comparison with SVM model.

From: PHDcleav: a SVM based method for predicting human Dicer cleavage sites using sequence and secondary structure of miRNA precursors

Methods Sn Sp Ac Mc AUC
SVM light ( Model 1 ) 84.32 88.46 86.40 0.730 0.922
Random Forest 80.54 90.81 85.68 0.717 0.921
Naïve Bayes 81.26 86.30 83.78 0.676 0.909
Simple CART 81.08 85.40 83.24 0.665 0.879
REP Tree 80.54 81.08 80.81 0.616 0.872
Random Tree 69.54 74.05 71.80 0.436 0.752
  1. All models were used for the prediction of cleavage site at 5p arm by using 'extended binary pattern' feature of 14 nt window taken from 'quikfold' derived structure (structure of CP-5p). Models were developed on training dataset using non-redundant 5-fold cross validation techniques.
  2. Sn: sensitivity, Sp: specificity, Ac: accuracy, Mc: Matthews correlation coefficient. AUC: area under curve.