# Integrating Multi-Omics Using Bayesian Ridge Regression with Iterative Similarity Bagging
## 4 Results and Discussion
### Effectiveness of BRR-ISB in Drug Response Prediction  
Different scenarios were implemented to assess the effectiveness of BRR-ISB in drug response prediction. (1) Baseline: the first scenario implemented the model with all 46,016 features of the three omics. (2) The model relied on the features produced for BRR after utilizing BRR as a feature selection method. (3) Similarity Network Fusion (SNF) [24] is one of the state-of-the-art methods for integrating genomic data. SNF works to create patient clustering for each available data type and then efficiently fuses these into one network that represents the full spectrum of underlying data. SNF can often achieve promising results compared with other methods, such as iCluster [88] or KMeans. SNF is a graph-embedding method in which transforming high-dimensional to new, low-dimensional representation may reduce the interpretability of a model as the extracted features are no longer biological measurements. Therefore, BRR-ISB was compared with SNF to evaluate how preserving biological context affects the model results. (4) BRR was combined with SNF. (5) Finally, different parameters were used to test BRR-ISB, as shown in Tables 5 and 6. All scenarios were implemented to the CCLE and GDSC dataset and drug chemical structure data, utilizing 100 epochs and 512 batches. \begin{table}
\begin{tabular}{l l} \hline \hline
**Type** & **Genes** \\ \hline \multirow{2}{*}{Gene expression} & TFP12, SGCE, ATP1B1, DSP, PEG10, MAGEA4, C1S, \\  & CPVL, GATA6, RP11-490M8.1 \\ \hline \multirow{2}{*}{Copy number alteration} & RASSF8-AS1, STARD3, PPP1R1B, SLC35E3, ZNF536, \\  & SOX5, TRIT1, TMEM75, ZNF879, ST8 SIA1 \\ \hline \multirow{2}{*}{Single-nucleotide mutation} & AKAP12, TP53, NLRP9, ATRX, OBSCN, CARD10, \\  & KRAS, ATR, FZD1, GPR112 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The top ten features for each multi-omics after implementation of ISB. The results from ISB pipeline experiments and BRR-ISB are nearly similar. However, the primary objective of the proposed method is to select informative features based on two relationships. Firstly, the relationship between drug response and multi-omics features is assessed using Bayesian Ridge Regression (BRR), a supervised feature selection method focused on domain-oriented contexts such as drug response. Secondly, the relationship among omics features themselves is measured using Iterative Similarity Bagging (ISB), an unsupervised method that eliminates similar genes according to distance. In general, features from the CCLE and GDSC were manually selected using the BRR-ISB or the ISB alone. Therefore, the determination of the bag size and the number of iterations requires that it is set automatically via a method such as GridSearchCV in scikit-learn. In addition, considering alternatives to BRR for feature selection methods may be considered to improve the proposed method. Combining all features in the baseline scenario caused memory crushing; therefore, a different GPU was utilized to solve the memory issue. BRR-ISB, employing a bag size of 200 features and ten iterations, demonstrated superior results regarding RMSE, PCC, and R\({}^{2}\) compared to other scenarios. It also significantly reduced the execution time by 77% compared to the BRR scenario. Similarity Network Fusion (SNF), a transformation-based