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Table 4 Prediction performance of different classifiers for PLP-interacting residues (PLPIRs)

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

Feature     Classifier SN SP ACC MCC
 Binary SVM (Threshold = −0.7) 77.02 ± 0.72 83.17 ± 0.27 82.62 ± 0.28 0.42 ± 0.01
SVM (Threshold = −0.5) 54.76 ± 1.34 95.81 ± 0.14 92.08 ± 0.18 0.51 ± 0.01
BayesNet 41.76 ± 0.81 88.94 ± 0.49 84.65 ± 0.40 0.26 ± 0.01
ComplementNaiveBayes 75.82 ± 1.74 77.14 ± 0.35 77.01 ± 0.23 0.34 ± 0.01
NaiveBayes 52.20 ± 1.50 91.18 ± 0.17 87.64 ± 0.20 0.37 ± 0.01
NaiveBayesMultinomial 59.25 ± 1.06 88.51 ± 0.19 85.85 ± 0.19 0.38 ± 0.01
IBk 40.02 ± 1.24 96.31 ± 0.20 91.19 ± 0.21 0.41 ± 0.01
  RandomForest 52.93 ± 1.09 80.03 ± 0.71 77.56 ± 0.65 0.23 ± 0.01
 PSSM SVM (Threshold = −0.7) 90.20 ± 1.04 92.61 ± 0.18 92.40 ± 0.13 0.67 ± 0.00
SVM (Threshold = −0.1) 79.76 ± 0.92 98.62 ± 0.13 96.91 ± 0.11 0.81 ± 0.01
BayesNet 77.66 ± 0.83 77.71 ± 0.35 77.70 ± 0.30 0.36 ± 0.01
ComplementNaiveBayes 76.28 ± 1.46 89.09 ± 0.54 87.93 ± 0.45 0.50 ± 0.01
NaiveBayes 79.40 ± 0.76 80.36 ± 0.35 80.28 ± 0.27 0.40 ± 0.00
NaiveBayesMultinomial 43.96 ± 0.67 98.16 ± 0.08 93.25 ± 0.07 0.52 ± 0.01
IBk 76.10 ± 0.82 98.80 ± 0.06 96.74 ± 0.08 0.79 ± 0.01
  RandomForest 62.27 ± 1.76 98.02 ± 0.12 94.78 ± 0.20 0.66 ± 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.