# Integrating Multi-Omics Using Bayesian Ridge Regression with Iterative Similarity Bagging
## 3 Materials and Methods
### Drug Response Prediction Using Graph Convolutional Network and Convolutional Neural Network  
Personalized oncology or medicine is a cancer therapy method aimed at identifying the most effective therapeutic solutions for each patient. This discovery was facilitated by combining genetic and drug sensitivity data, and the subsequent creation of drug response associations allowed this discovery. While personalized medicine is not yet utilized as a regular treatment, it is possible for most cancer patients due to the progress in multi-omics features and drug sensitivity testing [72]. Personalized treatment regimens based on genetics are a primary goal of systems medicine. For the development of individualized cancer therapy treatments with a projected efficacy much above existing standard-of-care methods, inferred models' ability to correctly forecast a tumor's responsiveness to a medicine or drug combination might benefit that process. Various methods for predicting drug response rely on genetic profiling data [72]. If a training set has experimentally measured genomic characteristics (such as protein expression, RNA expression, single-nucleotide polymorphisms (SNPs), DNA methylation, or other types) and responses to different drugs, then supervised or unsupervised approaches can be developed for each drug, considering one or more omics data features based on these assumptions [51]. Accurately predicting how individual patients will respond to drugs is a critical focus within the realm of personalized medicine [73]. By exploring the intricate interplay among multi-omics data derived from cancer cell lines or patient tumors, novel opportunities arise for developing individualized therapeutics tailored to each cancer patient [72]. Thus, drug response prediction was employed to evaluate the proposed model. BRR-ISB can also be applied across different domains. BRR-ISB integrates three omics: gene expression, single-nucleotide mutations, and copy number variations. This integrated multi-omics approach, combined with the drug's chemical structure, served as inputs to the drug response model shown in Figure 3.  
Graph Convolutional Networks (GCNs) have increasingly been employed to handle drug chemical structures due to the capability of the GCNs to model intricate relationships in molecular graphs. In this context, GCNs represent each molecule as a graph, where atoms are nodes and chemical bonds are edges. GCNs perform convolutional operations on these graphs, pooling features of each atom with those of its neighboring atoms to reproduce a specific representation of the atom within the molecule. This approach enables  
Figure 2: The architecture of the Iterative Similarity Bagging method. the model to consider both local atomic features and the global molecular structure, thereby making accurate predictions of molecular properties and activities. GCNs have demonstrated success in various tasks, such as predicting molecular properties, bioactivity, and pharmacokinetic properties, outperforming traditional machine learning methods that rely on hand-crafted features [63; 74]. For instance, [PERSON] et al. [63] showcased the effectiveness of GCNs on MoleculeNet, a benchmark dataset for molecular machine learning, reporting significant improvements in predictive performance across multiple datasets. GCNs facilitate efficient learning from graph representations of chemical structures, proving to be a powerful tool for drug discovery and development. The architecture of the proposed model was inspired by [37; 62] and customized to fit the CCLE dataset and integrate multi-omics. The Graph Convolutional Network (GCN) model is specifically designed to combine and analyze graph-structured and sequential data. This allows the model to predict intricate biological events such as drug response prediction. The model combines graph convolutional layers with 1D convolutional neural network layers. The graph component of the model processes drug features, represented as nodes in a graph.