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Table 1 The performance of models based on both positive dataset and negative 1 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 0.7 0.626 0.890 0.758 0.670 0.860 0.765
N5C5 0.7 0.665 0.866 0.766 0.640 0.850 0.745
k-space = 0 1.0 0.644 0.726 0.685 0.640 0.910 0.775
k-space = 1 1.0 0.639 0.704 0.672 0.610 0.930 0.770
k-space = 2 1.0 0.641 0.737 0.689 0.600 0.910 0.755
AAC + k-space = 0 0.9 0.693 0.907 0.800 0.690 0.900 0.795
AAC + N5C5 0.5 0.645 0.950 0.798 0.640 0.890 0.765
N5C5 + k-space = 0 0.9 0.678 0.907 0.792 0.630 0.860 0.745
AAC + N5C5 + k-space 0 0.5 0.641 0.978 0.810 0.610 0.910 0.760
PSSM 0.6 0.737 0.896 0.816 0.69 0.86 0.775