# Integrating Multi-Omics Using Bayesian Ridge Regression with Iterative Similarity Bagging
## 4 Results and Discussion
### Effectiveness of BRR-ISB in Drug Response Prediction
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline
**Method** & \begin{tabular}{l} **Input** \\ **Features** \\ \end{tabular} & \
\begin{tabular}{l} **Model** \\ **Features** \\ \end{tabular} &
\begin{tabular}{l} **Training** \\ \end{tabular} & \multicolumn{3}{l}{**Validation**} & \multicolumn{3}{l}{**Testing**} & \multicolumn{3}{l}{**Time**} \\ \hline  & & RMSE & PCC & R2 & RMSE & PCC & R2 & RMSE & PCC & R2 & \\ \hline Baseline & 36,725 & 36,725 & 0.023 & 0.934 & 0.872 & 0.032 & 0.879 & 0.771 & 0.03 & 0.89 & 0.791 & 19:38:19 \\ \hline  & & & \multicolumn{6}{c}{Iterative Similarity Bagging (ISB)} & \\ \hline ISB bag size = 50, & 36,725 & 11,987 & 0.022 & 0.943 & 0.889 & 0.031 & 0.881 & 0.776 & 0.03 & 0.895 & 0.8 & 59:48 \\ \hline ISB bag size = 50, & 36,725 & 10,367 & 0.023 & 0.935 & 0.874 & 0.031 & 0.88 & 0.774 & 0.03 & 0.895 & 0.799 & 51:44 \\ \hline ISB bag size = 100, & 36,725 & 4211 & 0.024 & 0.933 & 0.867 & 0.031 & 0.887 & 0.783 & 0.029 & 0.9 & 0.808 & 32:52 \\ \hline ISB bag size = 200, & 36,725 & 1174 & 0.026 & 0.919 & 0.843 & 0.031 & 0.881 & 0.775 & 0.03 & 0.894 & 0.798 & 26:07 \\ \hline ISB bag size = 300, & 36,725 & 974 & 0.025 & 0.92 & 0.846 & 0.031 & 0.883 & 0.78 & 0.029 & 0.896 & 0.803 & 24:31 \\ \hline ISB bag size = 400, & 36,725 & 956 & 0.025 & 0.92 & 0.846 & 0.031 & 0.882 & 0.777 & 0.029 & 0.896 & 0.801 & 24:43 \\ \hline  & & \multicolumn{6}{c}{Bayesian Ridge Regression with Iterative Similarity Bagging (BRR-ISB)} & \\ \hline BRR & 36,725 & 18,392 & 0.024 & 0.93 & 0.866 & 0.032 & 0.878 & 0.771 & 0.03 & 0.894 & 0.798 & 1:23:11 \\ \hline BRR-ISB bag size = 50, & 18,392 & 5369 & 0.023 & 0.938 & 0.879 & 0.031 & 0.885 & 0.783 & 0.029 & 0.899 & 0.807 & 36:19 \\ \hline BRR-ISB bag size = 50, & 18,392 & 4509 & 0.024 & 0.928 & 0.859 & 0.031 & 0.882 & 0.775 & 0.03 & 0.894 & 0.797 & 32:07 \\ \hline BRR-ISB bag size = 100, & 18,392 & 1681 & 0.026 & 0.915 & 0.835 & 0.031 & 0.881 & 0.774 & 0.03 & 0.892 & 0.794 & 23:28 \\ \hline BRR-ISB bag size = 200, & 18,392 & 606 & 0.026 & 0.916 & 0.838 & 0.031 & 0.883 & 0.777 & 0.029 & 0.896 & 0.801 & 19:58 \\ \hline BRR-ISB bag size = 300, & 18,392 & 549 & 0.028 & 0.904 & 0.817 & 0.032 & 0.878 & 0.771 & 0.03 & 0.892 & 0.796 & 21:08 \\ \hline BRR-ISB bag size = 400, & 18,392 & 566 & 0.028 & 0.903 & 0.815 & 0.032 & 0.877 & 0.769 & 0.03 & 0.892 & 0.796 & 21:33 \\ \hline  & & \multicolumn{6}{c}{Similarity Network Fusion} & \\ \hline SNF & 36,725 & 555 & 0.029 & 0.896 & 0.802 & 0.033 & 0.87 & 0.756 & 0.031 & 0.884 & 0.782 & 20:23 \\ \hline BRR-SNF & 18,392 & 555 & 0.029 & 0.894 & 0.799 & 0.033 & 0.869 & 0.756 & 0.031 & 0.884 & 0.781 & 20:19 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The scenarios were implemented using the same GDSC dataset and drug chemical structure for drug response prediction. features, which prevents overfitting and leads to better generalization [90; 91]. Furthermore, dropout is a technique designed to combat the overfitting problem [43]; dropout was utilized in the GCN-1 CNN model.