## 1 Introduction
Their method used a Support Vector Machine (SVM) classifier based on chemical features of nucleotides and position-specific nucleotide frequencies [20]. The i6 mA-Pred shows a good classification performance in rice 6 mA data. However, the association information among nucleotides near 6 mA sites is ignored. [PERSON] et al. proposed a new classification method called MM-6 mAPred based on a Markov model which makes use of the transition probability between adjacent nucleotides to identify 6 mA sites [21]. [PERSON] et al. built and evaluated their MM-6 mAPred based on the 6 mA-rice-Chen benchmark dataset. Their results show that MM-6 mAPred outperformed i6 mA-Pred in prediction of 6 mA sites. [PERSON] et al. developed a novel computational predictor, called SDM6A, which explores various features and five encoding methods to identify the DNA 6 mA sites [22]. [PERSON] et al. also trained and evaluated their SDM6A based on the 6 mA-rice-Chen benchmark dataset, and they found that SDM6A outperformed i6 mA-Pred on the 6 mA-rice-Chen benchmark dataset. The above three prediction models are trained on the 6 mA-rice-Chen benchmark dataset including only the 880 rice 6 mA sites and 880 non-6 mA sites. Even though the above methods have improved the performance for identifying 6 mA sites, too few data sets have been adopted to fully reflect the whole genome and to build robust models. [PERSON] et al. developed a machine learning method of predicting 6 mA sites named iDNA6 mA-rice which was trained and evaluated on the dataset 6 mA-rice-Lv containing 154,000 6 mA sites and 154,000 non-6 mA sites in the rice genome [23]. iDNA6 mA-rice utilized Random Forest to perform the classification for identifying 6 mA sites after using mono-nucleotide binary encoding to formulate positive and negative samples. In recent years, deep learning has not only been developed as a new research direction in machine learning, but has also made a lot of achievements in data mining, machine translation, natural language processing, and other related fields. In the field of computational biology [24; 25; 26; 27; 28], deep learning has been widely applied [29; 30; 31], especially in solving the problems of genome sequence-based by convolutional neural networks (CNN). [PERSON] et al. proposed an intelligent computational model called iDNA 6 mA (5-step rule) which extracts the key features from DNA input sequences via the convolution neural network (CNN) to identify 6 mA sites in the rice genome [32]. [PERSON] et al. developed a simple and lightweight deep learning model named SNNRice6 mA to identify DNA 6 mA sites in the rice genome and showed its advantages over other methods [33]. [PERSON] et al. developed a deep learning framework named Deep6 mA to identify DNA 6 mA sites. Deep6 mA, composed of a CNN and a bidirectional LSTM (BLSTM) module, is shown to have a better performance than other methods above on 6 mA prediction [34]. Although the above methods have made great progress, their performance is still not satisfactory. Furthermore, most of the methods are designed for one specific species such as the rice genome. Although some methods provide the across species validation test, the results are not as good as the original species-specific model. In this paper, we proposed a novel deep learning method called 6 mAPred-MSFF to identify DNA 6 mA sites. In this predictor, we establish the model by integrating the inverted residual block, the multi-scale channel attention module (MS-CAM), Bi-directional Long Short Term Memory Network (Bi-LSTM), and attentional feature fusion (AFF). To capture more feature information, we use the inverted residual block to expand the input features to a high dimension and filter them with a lightweight depthwise convolution. After that, we project them back to a low-dimensional representation as the input of the multi-scale channel attention module (MS-CAM). Bi-LSTM is designed to capture the long dependent information in sequences. The features generated from the Bi-LSTM are fed into the AFF to be fused.