# A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction
## 3 Experiments and Results
### Effects of Hyperparameter Setting on Prediction Performance  
We first investigated how a hyperparameter \(\alpha\)affects the prediction performance, which controls the impact of the regularization term in loss function. Figure 3 shows the \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multicolumn{2}{c}{**Average of AUCs for Non-DPU Inputs**} & \multicolumn{2}{c}{**Targeted Side Effects (Ses)**} & \multicolumn{2}{c}{**Used Resources for Graph Construction**} & \multicolumn{1}{c}{**Used Data**} \\ \hline \multirow{4}{*}{Proposed model} & 0.912 in all targeted Ses. & \multirow{2}{*}{200 Ses} & Drug-protein (DP), and & \multirow{2}{*}{14,247 labeled drug pairs} & \multirow{2}{*}{284 labeled and 284 labeled} \\  & 0.963 in the 10 Ses with the & & & Protein-Protein (PP) & and \\  & & highest AUC. & & & single drugs. \\ \hline [PERSON] et al., (2018) [14] & 0.872 in all targeted Ses. & \multirow{2}{*}{964 Ses} & DP, PP and Drug-Drug & 63,473 labeled drug pairs \\  & & & & (DD) * & and \\ \hline [PERSON] et al., (2020) [15] & 0.97 in the 50 Ses with the & \multirow{2}{*}{50 or more Ses} & PP, DP, DD *, and & \multirow{2}{*}{14,247 labeled drug pairs from} \\  & & highest AUC. & & Drug-Enzyme & 389 drugs. \\ \hline [PERSON] et al., (2020) [11] & Apparently 0.99 in all targeted Ses. & Apparently 964 Ses & DD * (and perhaps also PP and DP) & 63,473 labeled drug pairs and negative sampling. \\ \hline [PERSON] et al., (2021) [7] & 0.947 in all targeted Ses. & \multirow{2}{*}{964 Ses} & DP, DD *, and & \multirow{2}{*}{14,247 labeled drug pairs.} \\  & & highest AUC. & & Semantic data. & 645 drugs. \\ \hline [PERSON] et al., (2021) [13] & 0.94 in the 10 Ses with the & \multirow{2}{*}{10 or more Ses} & DP and DD * & \multirow{2}{*}{14,247 labeled drug pairs.} \\  & & highest AUC. & & & \\ \hline [PERSON] et al., (2021) [10] & 0.949 in all targeted Ses. & \multirow{2}{*}{200 Ses} & PP *, DP *, DD *, and & \multirow{2}{*}{46,221 labeled drug pairs and negative sampling.} \\  & & & & ten links of multi-type & \\ \hline [PERSON] et al., (2021) [16] & 0.998 in all targeted Ses. & \multirow{2}{*}{964 Ses} & DP, PP and DD * & \multirow{2}{*}{63,473 labeled drug pairs.} \\ \hline [PERSON] et al., (2022) [8] & 0.996 in all targeted Ses. & \multirow{2}{*}{200 Ses} & DD * (and perhaps also DP and PP) & Apparently 63,473 labeled drug pairs and negative sampling. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison with previous studies on non-DPU polypharmacy side effects prediction. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model Type**} & \multicolumn{2}{c}{**Labeled Data for Training**} & \multicolumn{2}{c}{**Data for Regularization**} & \multirow{2}{*}{**AUC**} & \multirow{2}{*}{**AUC-PR**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**F1-Score**} \\ \cline{2-2} \cline{5-8}  & \multicolumn{1}{c}{**Drug**} & & & & & & \\ \hline Baseline & O & X & X & X & 0.893 & 0.887 & 0.821 & 0.818 \\ \hline Model 1 & O & O & X & X & 0.904 & 0.895 & 0.827 & 0.821 \\ \hline Model 2 & O & O & O & X & 0.912 & 0.900 & 0.835 & 0.824 \\ \hline Model 3 & O & X & O & X & 0.912 & 0.901 & 0.837 & 0.827 \\ \hline Model 4 & O & X & O & O & 0.912 & 0.901 & 0.836 & 0.827 \\ \hline Model 5 (Proposed) & O & O & O & O & 0.912 & 0.901 & 0.837 & 0.828 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Non-DPU side effects prediction performance for the 200 targeted side effects with respect to model characteristics: Here AUC and AUC-PR are presented as average values for 200 side effects. The hyperparameter \(\alpha\)was set to 0.1 at which our model showed highest performance in predicting non-DPU side effects. change in prediction performance according to the value of \(\alpha\). The larger this value is, the greater the influence of regularization based on connectivity strengths between drugs and proteins on model learning, while the lesser the influence of side effect labels.