## 2 Materials and Methods
### Architecture of 6 mAPred-MSFF  
In this work, we proposed a new predictive model based on a multi-scale feature fusion mechanism. An overview of our model architecture is illustrated in Figure 1. There are four modules in our model including the sequence embedding module, the feature extraction module, feature fusion module, and prediction module. First, in the sequence embedding module, we use four different encoding schemes (1-gram, _NAC_, 2-grams, _DNC_) for the input of the embedding layer. Each nucleotide is represented as a dense vector of float point values. As a result, the genomic sequences can be represented by four feature matrices, respectively. We concatenate the feature matrices of the 1-gram and _NAC_ and the feature matrices of the 2-grams and _DNC_ matrices, respectively. Second, the resulting two feature matrices are fed into the feature extraction module which consists of the inverted residual block and the multi-scale channel attention mechanism module (MS-CAM). To extract more information from the global features and filter the features as the source of the MS-CAM, we use the inverted residual block including an expansion point-wise layer, a convolution layer, and a projection point-wise layer. The MS-CAM extract and combine the global and local features of the two feature matrices, respectively. Before fusing the two feature matrices, we use the bidirectional LSTM layer to learn the information about the long-distance dependence. Afterward, in the feature fusion module, we combine the features extracted from the bidirectional LSTM layer by adding operation and feed them into the MS-CAM module to calculate the fusion weight of the two feature matrices. We multiply the features matrices with the corresponding fusion weights and combine the results by adding operation. In this way, we obtain the features by aggregating the local and global contexts of the four different encoding schemes. Third, in the prediction module, the outputs of the features are passed through dropout layers for the prevention of overfitting during the training. After the dropout layer, to unstack the output, a flatten function was used to squash the feature vectors from the previous layers. Right after a flattened layer, a fully connected (FC) dense layer used as Dense(n), with the number of n neurons set as 32. At last, an FC layer was applied and used a sigmoid function for the binary classification. Sigmoid is used to squeeze the values between the range of 0 and 1 to represent the probability of having 6 mA and non-6 mA sites.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Positives** & **Negatives** & **Total** \\ \hline
6 mA-rice-Lv & 154,000 & 154,000 & 300,800 \\
6 mA-rice-Chen & 880 & 880 & 1760 \\ _A. thaliana_ & 15,937 & 15,937 & 31,874 \\ _R. chinensis_ & 11,815 & 11,815 & 23,630 \\ _F. vesca_ & 22,700 & 22,700 & 45,400 \\ _H. sapiens_ & 18,335 & 18,335 & 36,670 \\ _D. melanogaster_ & 11,191 & 11,191 & 22,382 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of seven benchmark datasets of six species.  
The 6 mAPred-MSFF is trained by the Adam optimizer and the binary cross-entropy loss function. Via giving a prediction score, the 6 mAPred-MSFF can determine whether the 6 mA sites are detectable in DNA sequences. It is detectable if the prediction score is >0.5; otherwise, it is not. The model is implemented using Keras 2.4.3. Below, each of ourmodules will be described in detail.