## 3 Results and Discussion
### Performance Comparison on Seven Benchmark Datasets
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Benchmark Dataset** & **Method** & **SN (\%)** & **SP (\%)** & **ACC (\%)** & **MCC** & **AUC** \\ \hline \multirow{4}{*}{6m\(A\)-rice-Lv} & 6 mAPred-MSFF & 97.88 & 94.64 & 96.26 & 0.926 & 0.989 \\ \cline{2-7}  & Deep6 mA & 94.73 & 93.72 & 94.23 & 0.885 & 0.981 \\ \cline{2-7}  & SNNRice6 mA-large & 94.97 & 92.22 & 93.59 & 0.872 & 0.978 \\ \cline{2-7}  & MM-6 mAPred & 93.27 & 88.84 & 91.05 & 0.822 & 0.955 \\ \hline \multirow{4}{*}{6m\(A\)-rice-Chen} & 6 mAPred-MSFF & 96.59 & 89.43 & 93.01 & 0.862 & 0.976 \\ \cline{2-7}  & Deep6 mA & 86.70 & 93.75 & 90.22 & 0.807 & 0.958 \\ \cline{2-7}  & SNNRice6 mA-large & 87.61 & 86.70 & 87.16 & 0.743 & 0.942 \\ \cline{2-7}  & MM-6 mAPred & 87.50 & 88.86 & 88.18 & 0.764 & 0.943 \\ \hline \multirow{4}{*}{_A. thaliana_} & 6 mAPred-MSFF & 89.92 & 87.85 & 88.88 & 0.778 & 0.954 \\ \cline{2-7}  & Deep6 mA & 84.72 & 89.52 & 87.12 & 0.743 & 0.942 \\ \cline{2-7}  & SNNRice6 mA-large & 85.28 & 88.15 & 86.71 & 0.735 & 0.939 \\ \cline{2-7}  & MM-6 mAPred & 82.62 & 88.82 & 83.82 & 0.664 & 0.904 \\ \hline \multirow{4}{*}{_R. chinensis_} & 6 mAPred-MSFF & 95.61 & 96.56 & 96.09 & 0.922 & 0.988 \\ \cline{2-7}  & Deep6 mA & 94.97 & 96.66 & 95.81 & 0.917 & 0.987 \\ \cline{2-7}  & SNNRice6 mA-large & 91.96 & 94.55 & 93.25 & 0.865 & 0.977 \\ \cline{2-7}  & MM-6 mAPred & 92.27 & 90.72 & 91.50 & 0.830 & 0.959 \\ \hline \multirow{4}{*}{_E. vesca_} & 6 mAPred-MSFF & 98.79 & 98.44 & 98.62 & 0.972 & 0.998 \\ \cline{2-7}  & Deep6 mA & 98.35 & 97.10 & 98.48 & 0.970 & 0.998 \\ \cline{2-7}  & SNNRice6 mA-large & 97.72 & 97.65 & 97.72 & 0.955 & 0.996 \\ \cline{2-7}  & MM-6 mAPred & 95.13 & 94.72 & 94.93 & 0.899 & 0.980 \\ \hline \multirow{4}{*}{_H. sapiens_} & 6 mAPred-MSFF & 94.65 & 94.91 & 94.78 & 0.896 & 0.985 \\ \cline{2-7}  & Deep6 mA & 90.16 & 95.18 & 92.67 & 0.854 & 0.985 \\ \cline{2-7}  & SNNRice6 mA-large & 88.62 & 93.56 & 91.09 & 0.823 & 0.972 \\ \cline{2-7}  & MM-6 mAPred & 88.08 & 87.22 & 87.65 & 0.753 & 0.937 \\ \hline \multirow{4}{*}{_D. melanogaster_} & 6 mAPred-MSFF & 97.08 & 96.72 & 96.90 & 0.938 & 0.990 \\ \cline{2-7}  & Deep6 mA & 95.12 & 95.04 & 95.08 & 0.902 & 0.984 \\ \cline{1-1} \cline{2-7}  & SNNRice6 mA-large & 94.64 & 93.36 & 94.00 & 0.880 & 0.982 \\ \cline{1-1} \cline{2-7}  & MM-6 mAPred & 90.65 & 89.94 & 90.30 & 0.806 & 0.955 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of the proposed method and 6 mA predictors based on seven datasets. For a more intuitive comparison, we further compared the ROC (receiver operating characteristic) curves and PR (precision-recall) curves of the four predictors, which are illustrated in Figures 2 and 3, respectively. We can observe that our method achieves the best values for an auROC of 0.989 and an auPRC of 0.981 on the 6 mA-rice-Lv dataset. We also achieve the best values for an auROC of 0.976 and auPRC of 0.973 on the 6 mA-rice-Chen dataset. For the other species, our proposed method also achieves the best values of auROC and auPRC. Therefore, we can conclude that our proposed method can achieve the best predictive performance for detecting 6 mA sites in rice species. Figure 2: The ROC curves (**A–G**) of four models (6 mAPred-MSFF, Deep6 mA, SNNRice6 mA-large, and MM-6 mAPred) based on the seven datasets (6 mA-rice-Lv, 6 mA-rice-Chen, _A. thaliana_, _R. chinensis_, _F. vesca_, _H. sapiens_, and _D. melanogaster_).