Table 6 Performance comparison of different classifiers using the F. vesca and R. chinensis datasets
From: N6-methyladenine identification using deep learning and discriminative feature integration
Species | Methods | ACC (%) | SN (%) | SP (%) | MCC |
|---|---|---|---|---|---|
F. vesca | NB | 83.40 | 88.81 | 78.41 | 0.722 |
RF | 84.10 | 81.51 | 88.28 | 0.734 | |
SVM | 85.94 | 81.24 | 86.74 | 0.735 | |
KNN | 89.69 | 92.22 | 85.65 | 0.753 | |
Deep-N6mA | 97.70 | 98.01 | 97.30 | 0.951 | |
R. chinensis | SVM | 78.57 | 78.03 | 83.92 | 0.677 |
RF | 81.33 | 81.51 | 76.82 | 0.705 | |
NB | 81.89 | 78.21 | 87.24 | 0.727 | |
KNN | 84.43 | 91.12 | 83.25 | 0.714 | |
Deep-N6mA | 95.75 | 96.45 | 94.55 | 0.921 |