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Table 2 Performance comparison of KNN and SVM-L on unseen sequences from the six datasets, λ1=0.5

From: Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach

Dataset ML Sens(%) Spec(%) Prec(%) Bal Acc(%) Acc(%) MCC AUC
DAMPD_AMP KNN 72.16 9 4 . 1 7 6 8 . 6 3 83.17 9 0 . 8 7 0 . 6 5 0 0.832
  SVM-L 7 7 . 3 2 a 91.62 61.98 8 4 . 4 7 89.47 0.631 0 . 8 4 5
APD3_AMP KNN 70.82 9 2 . 1 1 6 5 . 1 0 81.47 8 8 . 4 5 0 . 6 0 9 0.815
  SVM-L 8 9 . 2 4 82.87 51.98 8 6 . 0 5 83.97 0.597 0 . 8 6 1
DAMPD_ANTIBACTERIAL KNN 8 0 . 0 90.91 60.27 8 5 . 4 5 89.30 0.634 0 . 8 5 5
  SVML 74.55 9 3 . 1 0 6 5 . 0 8 83.82 9 0 . 3 7 0 . 6 4 0 0.838
APD3_ANTIBACTERIAL KNN 65.97 9 3 . 9 1 6 8 . 3 5 79.94 89.26 0.607 0.799
  SVM-L 8 1 . 9 4 91.55 65.92 8 6 . 7 5 8 9 . 9 5 0 . 6 7 6 0 . 8 6 7
DAMPD_BACTEROCIN KNN 8 0 87.50 50.00 8 3 . 7 5 86.49 0.561 0 . 8 3 8
  SVM-L 60 9 6 . 8 8 7 5 . 0 0 78.44 9 1 . 8 9 0 . 6 2 6 0.784
APD3_BACTEROCIN KNN 75.86 9 4 . 2 3 70.97 85.05 91.35 0.682 0.850
  SVM-L 9 3 . 1 0 92.95 7 1 . 0 5 9 3 . 0 3 9 2 . 9 7 0 . 7 7 4 0 . 9 3 0
  1. *Each value is the performance on the testing dataset by the classifier built by the machine learning algorithm (second column) on the dataset after applying the best compromise solution for λ1=0.5 (first column)
  2. aBold font indicates the best value per measure for every dataset