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Table 1 Prediction performance of different classifiers for vitamin-interacting residues (VIRs)

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) 68.57 ± 0.60 64.88 ± 0.18 65.22 ± 0.21 0.20 ± 0.00
SVM (Threshold = −0.5) 29.53 ± 0.83 94.71 ± 0.16 88.78 ± 0.15 0.27 ± 0.01
BayesNet 54.76 ± 1.44 69.64 ± 0.99 68.29 ± 0.85 0.15 ± 0.01
ComplementNaiveBayes 67.57 ± 0.90 65.16 ± 0.29 65.38 ± 0.33 0.19 ± 0.01
NaiveBayes 35.65 ± 0.85 89.52 ± 0.22 84.62 ± 0.18 0.22 ± 0.01
NaiveBayesMultinomial 40.08 ± 1.04 87.67 ± 0.24 83.35 ± 0.24 0.22 ± 0.01
IBk 26.67 ± 0.76 93.83 ± 0.11 87.73 ± 0.15 0.22 ± 0.01
  RandomForest 35.48 ± 0.78 79.13 ± 0.36 75.17 ± 0.31 0.10 ± 0.01
 PSSM SVM (Threshold = −0.8) 78.52 ± 0.64 78.61 ± 0.34 78.60 ± 0.32 0.37 ± 0.01
SVM (Threshold = −0.1) 52.19 ± 1.01 96.79 ± 0.03 92.73 ± 0.11 0.53 ± 0.01
BayesNet 67.41 ± 0.24 64.20 ± 0.06 64.49 ± 0.05 0.19 ± 0.00
ComplementNaiveBayes 61.21 ± 0.58 78.06 ± 0.23 76.53 ± 0.19 0.26 ± 0.00
NaiveBayes 67.64 ± 0.37 65.48 ± 0.11 65.68 ± 0.09 0.20 ± 0.00
NaiveBayesMultinomial 54.91 ± 0.94 83.52 ± 0.21 80.92 ± 0.16 0.28 ± 0.01
IBk 50.70 ± 0.90 96.91 ± 0.06 92.71 ± 0.08 0.52 ± 0.01
  RandomForest 61.54 ± 0.64 81.52 ± 0.12 79.70 ± 0.11 0.30 ± 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.