## 3 Results and Discussion
### Performance Impact by Different Encoding Schemes and Feature Descriptors
For a more intuitive comparison, we further compared the ROC (receiver operating characteristic) curves and PR (precision-recall) curves of the four feature descriptors, which are illustrated in Figures 4 and 5, respectively. We can observe that our proposed method achieves the best values of an auROC of 0.986 and an auPRC of 0.984 under the NCP. Our proposed method also achieves the best values of an auROC and auPRC under other feature descriptors. Therefore, we can conclude that our proposed method has a better ability to capture feature information. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **SN (\%)** & **SP (\%)** & **ACC (\%)** & **MCC** & **AUC** \\ \hline
6 mAPred-MSFF & 97.88 & 94.64 & 96.26 & 0.926 & 0.989 \\ \hline
1-gram & 97.15 & 93.68 & 95.42 & 0.909 & 0.987 \\ \hline
2-grams & 95.63 & 93.56 & 94.59 & 0.892 & 0.983 \\ \hline
3-grams & 96.10 & 93.60 & 94.85 & 0.897 & 0.984 \\ \hline
1-gram and 2-grams & 97.05 & 94.67 & 95.86 & 0.917 & 0.988 \\ \hline
1-gram and 3-grams & 95.97 & 93.49 & 94.73 & 0.895 & 0.983 \\ \hline
2-grams and 3-grams & 95.77 & 94.51 & 95.14 & 0.903 & 0.985 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of the proposed method on different encoding schemes based on the 6 mA-rice-Lv dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Feature** & **Method** & **SN (\%)** & **SP (\%)** & **ACC (\%)** & **MCC** & **AUC** \\ \hline \multirow{6}{*}{
\begin{tabular}{c} Kmer \\ (1 mer + 2 mer + 3 mer) \\ \end{tabular} } & 6 mAPred-MSFF & 67.17 & 64.94 & 66.08 & 0.321 & 0.719 \\ \cline{2-6}  & Deep6 mA & 69.00 & 60.35 & 64.67 & 0.294 & 0.700 \\ \cline{2-6}  & RF & 66.96 & 62.33 & 64.65 & 0.293 & 0.697 \\ \cline{2-6}  & ANN & 66.87 & 62.35 & 64.61 & 0.293 & 0.697 \\ \hline \multirow{6}{*}{NCP} & 6 mAPred-MSFF & 96.05 & 94.56 & 95.31 & 0.906 & 0.986 \\ \cline{2-6}  & Deep6 mA & 95.82 & 92.95 & 94.38 & 0.888 & 0.983 \\ \cline{2-6}  & RF & 92.90 & 91.51 & 92.20 & 0.844 & 0.966 \\ \cline{2-6}  & ANN & 92.92 & 91.53 & 92.22 & 0.845 & 0.966 \\ \hline \multirow{6}{*}{EIIP} & 6 mAPred-MSFF & 95.35 & 91.10 & 93.22 & 0.865 & 0.975 \\ \cline{2-6}  & Deep6 mA & 94.20 & 88.77 & 91.48 & 0.831 & 0.966 \\ \cline{2-6}  & RF & 92.56 & 91.21 & 91.89 & 0.838 & 0.964 \\ \cline{2-6}  & ANN & 92.61 & 91.15 & 91.88 & 0.838 & 0.964 \\ \hline \multirow{6}{*}{ENAC} & 6 mAPred-MSFF & 95.34 & 91.89 & 93.62 & 0.872 & 0.979 \\ \cline{2-6}  & Deep6 mA & 95.35 & 90.29 & 92.82 & 0.858 & 0.972 \\ \cline{1-1} \cline{2-6}  & RF & 90.82 & 91.16 & 90.99 & 0.820 & 0.959 \\ \cline{1-1} \cline{2-6}  & ANN & 90.80 & 91.17 & 90.99 & 0.820 & 0.959 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of methods based on the 6 mA-rice-Lv dataset under different feature descriptors. Figure 4: The ROC curves (**A**–**D**) of four models (6 mAPred-MSFF, Deep6 mA, SNNRice6 mA-large, and MM-6 mAPred) based on four feature descriptors (Kmer, NCP, EIIP, ENAC). Figure 5: The PR curves (**A**–**D**) of four models (6 mAPred-MSFF, Deep6 mA, SNNRice6 mA-large, and MM-6 mAPred) based on four feature descriptors (Kmer, NCP, EIIP, ENAC).