# A Novel Deep Learning Method for Predicting RNA-Protein Binding Sites
## 3 Results
### Prediction Results for the Original Dataset  
As shown in Table 2, HPNet achieved the best mean AUC of 0.945 with the RBP-24 dataset, 6.5% higher than GraphProt, 4.8% higher than Deepnet-rbp, 1.5% higher than iDeepE, and 2.8% higher than DeepPN. Moreover, HPNet achieved the best AUCs with 21 sub-datasets, with AUCs of 0.95 or higher with 16 sub-datasets. Among these baseline models, iDeepE and DeepPN only use RNA sequence features for model training, limiting their ability to learn RNA structure information and decreasing prediction accuracy. GraphProt adds RNA secondary structure information to the model, expressed as a graph structure. However, GraphProt uses machine-learning methods to train the model, which depend on pre-processing features and may cause problems, such as feature loss. The tertiary structure of RNA is predicted based on the secondary structure. Deepnet-rbp incorporates the tertiary structure into the model, and all three dimensions indicate binding specificity. However, the prediction of RNA tertiary structure is challenging and uncertain, which may also lead to feature loss and decreased accuracy. HPNet introduces the concept of hierarchical pooling in its utilization of secondary structures. It has been shown experimentally that RNA hierarchical features play an essential role in RBP-RNA interaction. For example, for the ZC3H7B protein, HPNet achieved a 1.9% improvement over iDeepE and a 13% improvement over Deepnet-rbp. Notably, HPNet performed better with RBPs with smaller training sets, such as ALKBH5, which has 2410 training samples, and C17 ORF85, which has 3709 training samples. Figure 4 depicts the results of a statistical comparison of the four methods and HPNet. If the AUC of HPNet was higher than that of the baseline method with the same sub-dataset, it is marked with \".\". Otherwise, it is marked with \"x\". Each dotted line in the figure represents a difference of 1%, and the p-value represents the significance test level. Generally, two methods are significantly different when the p-value is less than or equal to 0.05. A \(p\)-value of less than 0.001 indicates a highly significant difference. Figure 4 shows that the p-values for all four experiments were significantly lower than 0.001, demonstrating that HPNet is markedly different from the other four methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**RBPs** & **GraphProt** & **Deepnet-rbp** & **iDeepE** & **DeepPN** & **HPNet** \\ \hline ALKBH5 & 0.68 & 0.714 & 0.758 & 0.66 & **0.78** \\ C17 ORF85 & 0.800 & 0.820 & 0.830 & 0.837 & **0.893** \\ C22 ORF28 & 0.751 & 0.792 & 0.837 & 0.785 & **0.856** \\ CAPRIN1 & 0.855 & 0.834 & 0.893 & 0.886 & **0.912** \\ AGO2 & 0.765 & 0.809 & 0.884 & 0.868 & **0.895** \\ ELAVL1H & 0.955 & 0.966 & 0.979 & 0.978 & **0.994** \\ SFRS1 & 0.898 & 0.931 & 0.946 & 0.936 & **0.957** \\ HNRNRPC & 0.952 & 0.962 & 0.976 & 0.977 & **0.983** \\ TDP43 & 0.874 & 0.876 & 0.945 & 0.936 & **0.957** \\ TIA1 & 0.861 & 0.891 & 0.937 & 0.928 & **0.958** \\ TIAL1 & 0.833 & 0.870 & 0.934 & 0.926 & **0.953** \\ AGO1-4 & 0.895 & 0.881 & 0.915 & 0.912 & **0.939** \\ ELAVL1B & 0.935 & 0.961 & 0.971 & **0.976** & 0.975 \\ ELAVL1A & 0.959 & 0.966 & 0.964 & 0.967 & **0.973** \\ EWSR1 & 0.935 & 0.966 & 0.969 & 0.954 & **0.975** \\ FUS & 0.968 & 0.980 & 0.985 & 0.977 & **0.986** \\ ELAVL1C & 0.991 & **0.994** & 0.988 & **0.994** & 0.992 \\ IGF2 BP1-3 & 0.889 & 0.879 & 0.947 & 0.928 & **0.973** \\ MOV10 & 0.863 & 0.854 & 0.916 & 0.904 & **0.927** \\ PUM2 & 0.954 & 0.971 & 0.967 & 0.952 & **0.982** \\ QKI & 0.957 & **0.983** & 0.970 & 0.975 & 0.981 \\ TAF15 & 0.970 & 0.983 & 0.976 & 0.974 & **0.986** \\ PTB & 0.937 & **0.983** & 0.944 & 0.938 & 0.958 \\ ZC3H7B & 0.820 & 0.796 & 0.907 & 0.898 & **0.916** \\ \hline Mean & 0.887 & 0.902 & 0.931 & 0.919 & **0.945** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of HPNet and baseline methods with RBP-24 dataset. Figure 4: Comparison of the areas under the curve (AUCs) of HPNet and four methods with the debiased dataset.