# A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction
## 3 Experiments and Results
### Non-DPU Side Effect Prediction  
Data splitting procedure: We split all labeled drug pair samples into training, validation, and test datasets to evaluate prediction performance with non-DPU inputs. Labeled drug pairs were randomly divided into five folds for cross-validation. Drug pairs of four folds were used as labeled samples in the training data. Of the remaining fold drug pairs, 50% was used as the test data and the other 50% as the validation data. By observation, almost all drugs were included in the training data as well as in the test data. Virtual drug pairs were generated in the same manner as done for the DPU side effects prediction. Performance evaluation: The proposed method showed reasonably good performance with AUC of 0.912 also in non-DPU prediction (see Table 3). As with the DPU prediction, our model characteristics using labeled single drug data and a regularization term in the loss function have been shown to help improve the generalizability of the predictive model to some degree. However, unlike the DPU prediction, using virtual drug pairs for regularization did not affect prediction performance at all. Comparison with previous studies: We conducted a comparative evaluation of models reported in previous studies with respect to the prediction performance of non-DPU side effects. The studies to be used for the comparison were those that included label data from the TWOSIDES database [31] based on a procedure similar to that used in our study. The comparison results are given in Table 4. Here, our model showed AUC of 0.912 on average in the 200 targeted Ses while showing AUC of 0.963 in the 10 Ses with the highest AUCs. The study by [PERSON] et al. [13] included sample data that was most similar to the data used in our study, yet our proposed method here showed slightly better performance than that of [PERSON]'s with AUC of 0.94 in the 10 Ses with the highest AUCs. In addition, our model performed better in the averaged AUC for targeted side effects (0.912) compared to \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{6-9}  & & & & & & & & \\ \hline Baseline & O & X & X & X & 0.618 & 0.618 & 0.576 & 0.567 \\ \hline Model 1 & O & O & X & X & 0.642 & 0.645 & 0.588 & 0.575 \\ \hline Model 2 & O & O & O & X & 0.684 & 0.673 & 0.600 & 0.584 \\ \hline Model 3 & O & X & O & X & 0.690 & 0.683 & 0.616 & 0.602 \\ \hline Model 4 & O & X & O & O & 0.685 & 0.670 & 0.602 & 0.582 \\ \hline Model 5 & O & O & O & O & 0.708 & 0.697 & 0.637 & 0.618 \\ (Proposed) & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \end{tabular}
\end{table} 
Table 1: DPU side effects prediction performance for the 200 targeted side effects with respect to model characteristics. The performance is based on AUC (Area under the receiver operating characteristic curve) and AUC-PR (Area under the curve of Precision-Recall) averaged over 200 side effects. The hyperparameter \(\alpha\)was set to 0.7 at which our model showed highest performance in predicting DPU side effects.