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Table 2 Prediction performance of different classifiers for vitamin A-interacting residues (VAIRs)

From: Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information

Feature     Classifier SN SP ACC MCC
 Binary SVM (Threshold = −0.8) 61.92 ± 2.63 65.09 ± 0.43 64.80 ± 0.35 0.16 ± 0.02
SVM (Threshold = −0.1) 7.43 ± 1.18 99.66 ± 0.10 91.28 ± 0.08 0.21 ± 0.02
BayesNet 14.50 ± 2.11 94.30 ± 0.20 87.04 ± 0.22 0.10 ± 0.02
ComplementNaiveBayes 62.09 ± 0.50 65.97 ± 0.22 65.61 ± 0.20 0.17 ± 0.00
NaiveBayes 32.53 ± 0.99 86.43 ± 0.22 81.53 ± 0.27 0.15 ± 0.01
NaiveBayesMultinomial 60.23 ± 0.82 67.94 ± 0.16 67.24 ± 0.15 0.17 ± 0.01
IBk 31.41 ± 2.27 89.80 ± 0.20 84.49 ± 0.19 0.19 ± 0.02
  RandomForest 36.07 ± 2.03 78.38 ± 0.16 74.54 ± 0.30 0.10 ± 0.01
 PSSM SVM (Threshold = −0.8) 72.70 ± 2.87 76.89 ± 0.25 76.51 ± 0.37 0.32 ± 0.02
SVM (Threshold =0.0) 42.75 ± 1.08 97.51 ± 0.10 92.54 ± 0.13 0.48 ± 0.01
BayesNet 57.25 ± 1.21 69.54 ± 0.52 68.42 ± 0.48 0.16 ± 0.01
ComplementNaiveBayes 59.30 ± 1.23 66.96 ± 0.33 66.26 ± 0.26 0.16 ± 0.01
NaiveBayes 63.03 ± 1.65 69.09 ± 0.46 68.54 ± 0.56 0.19 ± 0.01
NaiveBayesMultinomial 55.77 ± 1.32 70.95 ± 0.21 69.57 ± 0.26 0.17 ± 0.01
IBk 44.05 ± 0.49 94.65 ± 0.34 90.05 ± 0.27 0.39 ± 0.01
  RandomForest 24.17 ± 0.80 99.31 ± 0.08 92.49 ± 0.06 0.41 ± 0.01
  1. *Bold value indicates highest performance with balanced sensitivity and specificity.
  2. **Italic value indicates performance with highest MCC.
  3. The values of standard errors are also given with performances.