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Table 3 Prediction performance of different classifiers for vitamin B-interacting residues (VBIRs)

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) 73.22 ± 0.36 67.00 ± 0.49 67.57 ± 0.47 0.24 ± 0.00
SVM (Threshold = −0.6) 30.36 ± 0.62 96.69 ± 0.12 90.66 ± 0.11 0.33 ± 0.01
BayesNet 63.25 ± 0.56 66.23 ± 0.73 65.96 ± 0.62 0.18 ± 0.00
ComplementNaiveBayes 68.69 ± 0.52 68.51 ± 0.23 68.52 ± 0.18 0.23 ± 0.00
NaiveBayes 37.74 ± 0.90 90.45 ± 0.23 85.66 ± 0.14 0.25 ± 0.01
NaiveBayesMultinomial 44.22 ± 0.43 87.54 ± 0.24 83.60 ± 0.19 0.25 ± 0.00
IBk 30.81 ± 0.71 93.33 ± 0.17 87.65 ± 0.14 0.24 ± 0.01
  RandomForest 39.33 ± 1.08 79.36 ± 0.37 75.72 ± 0.36 0.13 ± 0.01
 PSSM SVM (Threshold = −0.8) 83.33 ± 0.36 80.51 ± 0.13 80.77 ± 0.14 0.42 ± 0.00
SVM (Threshold =0.1) 55.57 ± 0.63 98.04 ± 0.10 94.18 ± 0.09 0.61 ± 0.01
BayesNet 71.65 ± 1.13 66.14 ± 0.08 66.64 ± 0.10 0.23 ± 0.01
ComplementNaiveBayes 63.90 ± 1.26 81.73 ± 0.28 80.11 ± 0.22 0.32 ± 0.01
NaiveBayes 72.28 ± 1.22 66.44 ± 0.09 66.97 ± 0.12 0.23 ± 0.01
NaiveBayesMultinomial 21.22 ± 0.69 98.88 ± 0.03 91.82 ± 0.06 0.34 ± 0.01
IBk 56.74 ± 0.80 98.04 ± 0.07 94.28 ± 0.11 0.62 ± 0.01
  RandomForest 39.16 ± 0.56 97.74 ± 0.09 92.41 ± 0.10 0.46 ± 0.01
  1. *Bold value indicates highest SVM performance with balanced sensitivity and specificity.
  2. **Italic value indicates SVM/IBk performance with highest MCC.
  3. The values of standard errors are also given with performances.