# miRNA-Gene Association Prediction Method Based on Deep Learning Model
## 3 Results
### K-Fold Cross-Validation and Model Performance  
K-fold cross-validation is a statistical method for evaluating the performance of machine learning models. It works by dividing a dataset into k-folds and then training and evaluating the model multiple times, using each fold as the test data and the remaining folds as the training data. This method yields statistically reliable results for evaluating the performance of the model. In bioinformatic deep learning studies, where data are relatively scarce, k-fold cross-validation is often used to demonstrate the generalization performance of a model.  
We conducted a k-fold cross-validation to determine the generalization performance and statistical confidence of our model. We used the same 717,728 datasets as those in the aforementioned experiment with a fold of 5. We obtained test AUC values of 0.9819, 0.9807, 0.9834, 0.9732, and 0.9826 with an average AUC of 0.9804. The AUC and convergence metrics of the best-performing model are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Purpose** & **Data Name** & **Number of Data Elements** \\ \hline Model Training & Positive Data & 358,864 \\ and & Negative Data & 358,864 \\ Evaluation & Total Data & 717,728 \\ \hline Case Study & Unknown Data Pool & 37,672,400 \\ Generate Negative Data & Negative Candidate Pool & 4,932,554 \\ \hline Data & miRNA & 2656 \\ Components & Gene & 14,319 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Our data configuration.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **LSTM** & **Bi-LSTM** \\ \hline AUC & 0.98 & 0.936 \\ \hline \hline \end{tabular}
\end{table}
Table 5: AUC scores achieved by the LSTM and Bi-LSTM models.  
We used the AUC as a performance metric for the prediction model because it is the most widely used metric in the field and represents the overall performance of the model. The area under the receiver operating characteristic curve (AUC) is a measure that illustrates the relationship between the true positive rate (TPR, or sensitivity) for positive classes and the false positive rate (FPR, or 1--specificity) for negative classes at different classification thresholds. An AUC value closer to 1 indicates a superior prediction model. According to a review paper published in 2022 [3], which investigated 29 recent studies on miRNA-disease association prediction, among all the performance metrics, AUC is the only one presented as a performance measure in all 29 studies. We also present metrics  
Figure 6: Our model’s highest achieved AUC (0.9834) and corresponding confusion matrix. AUC is the most widely used evaluation metric in this field and represents the overall performance of the model, illustrating the relationship between the true positive rate (TPR, sensitivity) for the positive class and the false positive rate (FPR, 1—specificity) across different classification thresholds. As the AUC approaches 1, it is interpreted that the model is better at distinguishing the positive class from the negative class.  
such as precision, recall, accuracy, and F1 score. The performance values are summarized in Table 6. The highest AUC achieved is 0.9834 (Figure 6), which represents the best performance among state-of-the-art miRNA-gene association prediction deep learning studies; furthermore, it underscores the appropriateness of our data generation method and its capability to accurately represent the association between miRNAs and genes.