## 3 Results
### Investigation of Novel Drug-Disease Pairs  
In this section, we reveal the novel of candidate drug-disease pairs based on false positives of our method with high performance of more than 90% of AUC score. The 50 diseases with AUCs of more than 90% are shown in Table 1. The candidate drugs corresponding to these 50 diseases were ranked in descending order based on the functional similarity score. The functional similarity score is the maximum of Jaccard similarity score for the functions of a drug and all known drugs (see Section 3.3). We investigate these novel candidate drug-disease pairs that were incorrect positive predictions in more detail in this section. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Threshold** \\ **Range** \\ \end{tabular} } & \multicolumn{2}{c}{**AUC**} & \multicolumn{2}{c}{**F1**} & \multicolumn{2}{c}{**ACC**} \\ \cline{2-7}  & **Model** & **Model Using Important** & **Model** & **Model Using Important** & **Model** & **Model Using Important** \\  & **Using PPSVs** & **Features** & **Using PPSVs** & **Features** & **Using PPSVs** & **Features** \\ \hline \(\geq\)0.9 & 10.2\% & 23.8\% & 12.6 & 22.2\% & 0.4\% & 22.2\% \\ \(\geq\)0.8 & 35.9\% & 39.1\% & 59.1 & 55.0\% & 2.0\% & 45.8\% \\ \(\geq\)0.7 & 67.4\% & 58.6\% & 97.6 & 84.3\% & 15.2\% & 67.4\% \\ \(\geq\)0.6 & 85.7\% & 77.2\% & 100.0 & 100.0\% & 57.8\% & 83.3\% \\ \(\geq\)0.5 & 95.9\% & 88.7\% & 100.0 & 100.0\% & 94.3\% & 100.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Percentage of diseases meeting the indicated performance range for model using PPSVs and important features. Figure 6: Frequency of features in PPSVs satisfied the highest and second-highest linear correlations for all 478 diseases. The top 20 candidate drug-disease pairs were identified by ranking the functional similarity score of the candidate drugs and known drugs in descending order, as shown in Table 7. It was found that the average functional similarity score for the top 20 candidate drug-disease pairs was about 0.98. In other words, 20 candidate drugs for diseases with false positive labels were 98% similar to their known drugs. However, correct positive prediction of candidate drug-disease pairs (i.e., true positives) were 100% similar to their known drugs. This indicates that these 20 novel candidate drugs, with their high functional similarity score of 98% could be used to treat the corresponding disease. Novel of candidate drug-disease associations are useful for proposing alternative indications of existing drugs, assessing side effects, and determining potential drug resistance. Pergolide (DrugBank ID: DB01186) is dopamine receptor agonist used for the treatment of Parkinson disease [34]. In our investigation, we found that pergolide has a high functional similarity score with the known drug zuclopenthixol (DrugBank ID: DB01624), which is used to treat bipolar disorder, as shown in the first highlight. Some studies have investigated the possibility of the pergolide being indicated for the treatment of bipolar disease (MESH ID: D001714). [PERSON] and [PERSON] explored pergolide as a supplement to antidepressant therapy with tricyclic antidepressants and monoamine oxidase inhibitors. Pergolide successfully adjusted the mood, interest, and energy of 11 out to 20 bipolar patients within a week [35]. However, pergolide has been withdrawn from several markets, the US and Canadian markets, because it was found to increase the risk of cardiac valvalopathy [36]. Further, one of the top 20 drug-disease associations identified in this study suggested that isradipine (DrugBank ID: DB00270) is also related to bipolar disease (MESH ID: