## 3 Results and Discussion
### Performance Impact by Different Encoding Schemes and Feature Descriptors  
Our proposed model uses four different encoding schemes (1-gram, _NAC_, 2-grams, _DNC_) as the input of the embedding layer. The initial features will have an important impact on the training results of deep learning models. To explore the impact of different encoding methods, we will evaluate our proposed model with N-grams encoding scheme and different feature descriptors as the initial input. N-grams encoding. To validate the impact of the N-grams encoding, we compare our proposed model with the encoding schemes (1-gram, 2-grams, 3-grams 1-gram and 2-grams, 1-gram and 3-grams, 2-grams and 3-grams) based on the 6 mA-rice-Lv dataset. The prediction results are listed in Table 4. From Table 4, we can see that the 1-gram encoding scheme achieves an SN of 97.15% second only to our proposed method. The 1-gram and 2-gram encoding scheme achieves an SN of 94.47%, an SP of 94.67%, an ACC of 95.86, and an MCC of 0.917. Therefore, our proposed method uses the 1-gram and 2-gram encoding scheme integrating _NAC_ and _DNC_ to encode the input sequences. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Species** & **Method** & **SN (\%)** & **SP (\%)** & **ACC (\%)** & **MCC** & **AUC** \\ \hline \multirow{4}{*}{_R. chinensis_} & 6 mAPred-MSFF & 90.66 & 94.26 & 92.46 & 0.850 & 0.966 \\ \cline{2-7}  & Deep6 mA & 88.87 & 95.29 & 92.07 & 0.843 & 0.966 \\ \cline{2-7}  & SNNRice6 mA-large & 89.79 & 94.13 & 91.96 & 0.840 & 0.964 \\ \cline{2-7}  & MM-6 mAPred & 88.51 & 91.35 & 89.93 & 0.799 & 0.949 \\ \hline \multirow{4}{*}{_F. vesca_} & 6 mAPred-MSFF & 93.95 & 95.15 & 94.55 & 0.891 & 0.982 \\ \cline{2-7}  & Deep6 mA & 92.52 & 96.22 & 94.37 & 0.888 & 0.982 \\ \cline{2-7}  & SNNRice6 mA-large & 93.26 & 95.15 & 94.20 & 0.884 & 0.980 \\ \cline{2-7}  & MM-6 mAPred & 90.33 & 93.48 & 91.91 & 0.839 & 0.965 \\ \hline \multirow{4}{*}{_H. sapiens_} & 6 mAPred-MSFF & 72.51 & 93.33 & 82.92 & 0.673 & 0.884 \\ \cline{2-7}  & Deep6 mA & 68.82 & 94.44 & 81.63 & 0.655 & 0.881 \\ \cline{2-7}  & SNNRice6 mA-large & 70.93 & 93.22 & 82.07 & 0.658 & 0.894 \\ \cline{2-7}  & MM-6 mAPred & 71.85 & 88.67 & 80.26 & 0.614 & 0.873 \\ \hline \multirow{4}{*}{_D. melanogaster_} & 6 mAPred-MSFF & 74.30 & 92.62 & 83.46 & 0.680 & 0.903 \\ \cline{2-7}  & Deep6 mA & 68.81 & 93.78 & 81.30 & 0.646 & 0.901 \\ \cline{2-7}  & SNNRice6 mA-large & 70.73 & 92.27 & 81.50 & 0.645 & 0.906 \\ \cline{2-7}  & MM-6 mAPred & 64.96 & 88.84 & 76.90 & 0.554 & 0.868 \\ \hline \multirow{4}{*}{_A. thaliana_} & 6 mAPred-MSFF & 58.66 & 94.28 & 76.47 & 0.567 & 0.837 \\ \cline{2-7}  & Deep6 mA & 54.56 & 95.26 & 74.91 & 0.545 & 0.834 \\ \cline{1-1} \cline{2-7}  & SNNRice6 mA-large & 57.92 & 94.11 & 76.01 & 0.558 & 0.853 \\ \cline{1-1} \cline{2-7}  & MM-6 mAPred & 57.14 & 91.44 & 74.30 & 0.517 & 0.845 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of methods trained with the 6 mA-rice-Lv dataset on cross species. Feature descriptors. There are several feature descriptors including Kmer, NCP, EIIP, and ENAC which are employed in machine learning methods. To validate the impact of the different feature descriptors, we take the feature descriptors as the initial input of our proposed model trained on 6 mA-rice-Lv and perform the same evaluation on the Deep6 mA, Random Forest (RF), and Artificial Neural Network (ANN). The prediction results are listed in Table 5. From Table 5, we can see that our proposed method trained by the NCP achieves the best values of an SN of 96.05%, an SP of 94.56%, an ACC of 95.31%, and an MCC of 0.906. The models trained by Kmer which concatenate 1 mer, 2 mer, and 3 mer do not achieve satisfactory results. Meanwhile, we can observe that our proposed method outperforms all other methods in terms of SN, SP, ACC, and MCC under different feature descriptors.