# miRNA-Gene Association Prediction Method Based on Deep Learning Model
## 2 Materials and Methods
### Deep Learning Model  
In this study, we worked with sequence data generated from miRNA-gene pairs. To accurately capture the complex sequence features of miRNAs and genes for the prediction of their mutual associations, we employed specialized deep learning models that are tailored for the analysis of sequential data. RNNs (recurrent neural networks) [27] are a type of deep learning architecture tailored for sequential data. And LSTM (long short-term memory) [13], an advanced type of RNN, is designed to overcome the vanishing gradient issue inherent in basic RNNs. Therefore, we employed LSTM and Bi-LSTM [22] models, leveraging their ability to effectively remember and utilize long-range sequential  
Figure 4: For the positive data set, we employed a subset of experimentally validated miRNA-gene interaction pairs derived from the miRNABase. To construct the Unknown Data Pool, we generated a comprehensive set of possible interactions by combining all miRNAs and genes used in the positive data set, excluding any pairs that overlap with the positive data set to avoid redundancy. Subsequently, we initiated Three Distance-Based Filtering to establish a Negative Candidate Pool. From this Negative Candidate Pool, we randomly selected a number of pairs equal to that of the positive set to form our final negative data set, thereby ensuring a balanced representation of both classes for the training process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Count of Data Elements** & **Dataset** \\ \hline All Combined (mi \(\times\) Gene) & 38,031,264 & \\ Extract (-) Positive Data = & 37,672,400 & Unknown Data Pool \\ Three Distance-based Filtering = & 4,932,554 & Negative Candidate Pool \\ Randomly Selected (As Pos) = & 358,864 & Negative Data \\ \hline \hline \end{tabular}
\end{table}
Table 3: Process of generating negative data generation and the names of the corresponding datasets.  
information, which is crucial for understanding the complex interactions between miRNA and gene sequences. A pivotal component of the LSTM model is the cell state. The cell state undergoes an update process, which can be summarized as follows:  
1. Begin by multiplying the previous cell state value (\(C_{t-1}\)) with the output of the forget gate (\(f_{t}\)), determining what information is to be discarded.
2. Update the current cell state value by multiplying it with the output of the input gate. This operation gauges how much new information should be stored.
3. Add the updated current cell state value to the output of the input gate. This step identifies the information to be retained.  
A visual representation of this process can be observed in Figure 5.  
\[C_{t}=f_{t}\times C_{t-1}+i_{t}\times\widetilde{C}_{t}. \tag{4}\]  
Bi-LSTM is a deep learning architecture that extends LSTM to model bidirectional information within sequence data. Traditional LSTM processes input data from front to back. In contrast, Bi-LSTM processes input data in both forward and backward directions, ensuring that the model has access to future contexts when making predictions at a given point in the sequence. Consequently, combining bidirectional information improves the model's generalization performance and reduces overfitting. However, due to the doubled parameter count inherent to its bidirectional nature, Bi-LSTM is computationally intensive and learns slower than traditional LSTM.