# miRNA-Gene Association Prediction Method Based on Deep Learning Model
## 3 Results
### Validation of the Model  
A case study was conducted to demonstrate the utility and performance of the proposed model using the \"Unknown Data Pool\" (Table 3) generated in this study. The aim was to identify unknown positive candidates in addition to known positive data from miRNABase. From the \"Unknown Data Pool\", positive and negative data used for model training and evaluation were excluded. We predicted the association between all remaining miRNA-gene data pairs and identified the top 20 pairs based on their association scores from our model. The results are summarized in Table 7. We identified several pairs that were not designated as positive data in this research but are considered positive pairs in other datasets, specifically TargetScan [36] and miRWalk [37]. We also predicted candidate pairs that might have a positive relationship that was not identified in open datasets or other sources. This finding underscores the genuine value of our study, highlighting the potential to uncover novel pairs of associations using our data and models. We also identified miRNAs that are potentially associated with genes known to cause cancer. _BRCA2_ is a human gene strongly associated with the risk of breast and ovarian \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Precision** & **Recall** & **Accuracy** & **F1 Score** & **AUC** \\ \hline Fold-1 & 0.949 & 0.924 & 0.94 & 0.936 & 0.9819 \\ Fold-2 & 0.925 & 0.933 & 0.93 & 0.929 & 0.9807 \\ Fold-3 & 0.937 & 0.938 & 0.94 & 0.937 & 0.9834 \\ Fold-4 & 0.915 & 0.925 & 0.92 & 0.92 & 0.9732 \\ Fold-5 & 0.964 & 0.919 & 0.94 & 0.941 & 0.9826 \\ \hline Average & 0.938 & 0.9278 & 0.934 & 0.9326 & 0.9804 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The table of five-fold cross-validation prediction performance results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rank** & **miRNA** & **Gene** & **Support** \\ \hline
1 & hsa-miR-3913-5p & _ARL13B_ & \\
2 & hsa-miR-3913-5p & _RALGABA1_ & TargetScan [36] \\
3 & hsa-miR-3913-5p & _OTOGL_ & \\
4 & hsa-miR-32-5p & _NNAT_ & \\
5 & hsa-miR-3913-5p & _PNISR_ & \\
6 & hsa-miR-5697 & _FOXO6_ & \\
6 & hsa-miR-5697 & _GLRA3_ & TargetScan \\
7 & hsa-miR-3913-5p & _CPED1_ & \\
8 & hsa-miR-5196-3p & _EEG_ & TargetScan \\
9 & hsa-miR-5196-3p & _ZNF419_ & TargetScan \\
10 & hsa-miR-3913-5p & _ZNF644_ & \\
11 & hsa-miR-3913-5p & _ZSWIM6_ & \\
12 & hsa-miR-6893-3p & _STARD8_ & TargetScan, miRWalk [37] \\
13 & hsa-miR-5196-3p & _JADE1_ & \\
14 & hsa-miR-5697 & _TFEC_ & \\
15 & hsa-miR-548c-3p & _UBED24_ & TargetScan \\
16 & hsa-miR-548c-3p & _REPS2_ & TargetScan \\
17 & hsa-miR-5697 & _BMPR2_ & \\
18 & hsa-miR-5697 & _CDKN2B_ & \\
19 & hsa-miR-3913-5p & _KAT2B_ & \\
20 & hsa-miR-3913-5p & _ARL13B_ & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Top 20 miRNA candidates associated with genes from the Unknown Data Pool. cancers. _BRCA2_ codes for a protein that plays an important role in repairing DNA damage and helps regulate the cell cycle, preventing errors during DNA replication. Cells with unrepaired DNA damage can become malignant and develop cancer [38]. Women and men with _BRCA2_ mutations have an increased risk of breast cancer, ovarian cancer, and prostate cancer. Therefore, we predicted miRNAs associated with the _BRCA2_ using our model. Based on the association prediction score of our model, we present the top 1 miRNAs associated with _BRCA2_ (Table 8). We identified one miRNA associated with _BRCA2_ that is also supported by TargetScan. While not explicitly stated to have a direct association with _BRCA2_, we observed that miRNAs related to diseases closely associated with _BRCA2_, such as ovarian cancer, breast cancer, and prostate cancer, were included in the top 10 rankings. According to GeneCaRNA [39], in this experiment, hsa-miR-503-5p was ranked highest in predicted association with _BRCA2_, which is related to prostate cancer.