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Table 5 Performance of 3-way classification using SVM classifier

From: Plus ça change – evolutionary sequence divergence predicts protein subcellular localization signals

  Divergence Classical features Combination
  AUC MCC AUC MCC AUC MCC
MTS 0.67±0.03 0.36±0.06 0 . 8 7±0.03 0.76±0.05 0 . 8 7±0.03 0 . 7 7±0.03
SP 0.50±0.00 0.00±0.00 0.81±0.08 0.70±0.11 0 . 9 0±0.06 0 . 8 3±0.07
N-signal-free 0.66±0.02 0.36±0.03 0.85±0.03 0.72±0.05 0 . 8 7±0.02 0 . 7 7±0.03
% accuracy 70.82±1.61 87.24±1.86 8 9 . 3 0±0.66
  1. The 5-fold cross-validation performance of an SVM classifier using: divergence features only, classical features only, and the two combined; is shown for three-way classification on the yeast curated ortholog dataset. Classical features are computed based on the N-terminal 40 residues.