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Table 2 Performance of models based on both positive dataset and negative 2 dataset using SVM as classifier

From: Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides

Features Training Testing
Weight Sensitivity Specificity Accuracy Sensitivity Specificity Accuracy
AAC 0.6 0.873 0.952 0.913 0.860 0.942 0.928
N5C5 1.9 0.942 0.909 0.925 0.960 0.878 0.891
k-space = 0 0.4 0.799 0.957 0.878 0.750 0.952 0.918
k-space = 1 0.4 0.810 0.948 0.879 0.700 0.934 0.895
k-space = 2 0.5 0.840 0.957 0.898 0.720 0.958 0.918
AAC + k-space = 0 0.9 0.868 0.920 0.894 0.860 0.914 0.905
AAC + N5C5 0.4 0.892 0.972 0.932 0.940 0.952 0.950
N5C5 + k-space = 0 0.5 0.857 0.957 0.907 0.890 0.930 0.923
AAC + N5C5 + k-space 0 1.0 0.909 0.909 0.909 0.930 0.900 0.905
PSSM 0.7 0.909 0.911 0.913 0.910 0.838 0.850