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Table 7 Performance of 3-way classification using SVM classifier (feature length 20)

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 9±0.02 0.80±0.02 0 . 8 9±0.01 0 . 8 1±0.02
SP 0.50±0.00 0.00±0.00 0.97±0.03 0.92±0.07 0 . 9 8±0.03 0 . 9 7±0.04
N-signal-free 0.66±0.02 0.36±0.03 0 . 9 0±0.01 0.81±0.02 0 . 9 0±0.01 0 . 8 3±0.02
% accuracy 70.82±1.61 91.49±1.26 9 2 . 2 3±1.25
  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 our entire yeast curated ortholog dataset. Classical features are calculated from N-terminal 20 amino acids.