## 3 Results
### Gold Features Analysis  
This research proposed a method to investigate the relationship between target proteins and disease-related proteins based on several biological information, such as topological network, proteomic data, functional analysis, and druggable property, using PPSVs. The PPSVs consisted of seven features: the neighboring similarity score, the closeness score, the local alignment score, the global alignment score, the pathway score, the confident score, and the shared drug score. We investigated the gold features of our approach, which represent the best descriptors for the prediction results, using Pearson correlation coefficients (PCC). The PCC is used to measure the linear correlation between two variables. If a PPC is close to 1 or \(-1\), the two variables have a highly positive or negative linear correlation, respectively. We calculated the PCC score for the seven features of each disease-related protein and the labels of drug-disease pairs that were added to the drug-disease matrix for each disease. The absolute PCC scores were then ranked in descending order for each disease to produce a highly linear correlation of the seven features for each disease-related protein. We then repeated this process for all diseases. The number of features that satisfied the top two of the absolute score in PCC for all 478 diseases is presented in Figure 6.  
Figure 6 shows that the confidence score appeared in the highest and second highest PCC for 122 and 117 diseases, respectively, while the pathway score was found among the top two PPC for 113 and 85 diseases, respectively. The top two features for both the highest and second-highest PCC were the confidence and pathway scores, which were related to functional similarity in the PPSVs, thus these were identified as the gold features. The third feature most commonly appearing among the highest and second-highest PPC was the neighboring similarity score in the PPSVs. Therefore, two proteins that have a highly similar neighborhood might cooperate in the same module to regulate the same functional task. Figure 6 also reveals that the number of local alignment features ranked among the highest and second-highest PCC was higher than that of global alignment features. Hence, the local alignment score was more important for the model predictions than the global alignment score.  
Figure 5: Receiver operating characteristic (ROC) of our model compared with [PERSON]’s and [PERSON]’s methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance Values** & **[PERSON] (2018)** & **[PERSON] (2019)** & **Our Model** \\ \hline AUC & 0.932 & 0.909 & 0.989 \\ AUPR & 0.911 & 0.919 & 0.988 \\ PRE & 0.91 & 0.844 & 0.956 \\ REC & 0.866 & 0.826 & 0.940 \\ ACC & 0.885 & 0.830 & 0.947 \\ F1 & 0.888 & 0.835 & 0.948 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of our model compared with [PERSON]’s and [PERSON]’s methods.  
Interestingly, we ran our model using only the important features identified above, i.e., the confidence, pathway, neighboring similarity, and local alignment scores, which lead to better performance for a higher performance threshold (Table 6). In particular, using the important features to generate the model, the number of diseases that satisfied the performance threshold of \(\geq\)0.90 increased. However, the number of diseases decreased for the performance scores of AUC and F1 more than 70% (the threshold \(\geq\)0.70). Based on this, it can be concluded that these important features play a crucial biological role in identifying drug-disease associations, but some diseases require other biological information in the PPSV to be predicted. Thus, the final model was trained on all features for all diseases.