# Integrating Multi-Omics Using Bayesian Ridge Regression with Iterative Similarity Bagging
## 2 Related Work
[37] introduced Graph Transformer for Drug Response Prediction (GrapTransDRP). This innovative neural network structure can extract a more refined drug representation from molecular graphs to forecast drug responses on cell lines. The Graph Transformer was combined with the Graph Attention Network (GAT) and Graph Convolutional Networks (GCNs) to acquire knowledge about the features of drugs. Subsequently, 1D convolutional neural network (CNN) layers were employed for each omits data type to acquire latent gene expression, mutation, copy number aberration, and methylation features. [PERSON] et al. [38] proposed a late multi-omics integration framework for robustly quantifying survival and drug response in breast cancer patients. Each omics dataset was individually processed using a neighborhood component analysis (NCA)-based feature selection algorithm. The most important features identified were then utilized in classifier and regressor models based on neural networks. A summary of the shortcomings in the related studies reveals that the curse of dimensionality inherent in biological data affects most proposed models. Recent studies have independently processed each \"omic\" to reduce dimensionality and data complexity [34; 35], often exploring unsupervised methods, which may lose domain-specific context. Domains such as drug response or cancer classification are crucial in identifying informative features. Supervised feature selection approaches, such as those considering drug responses [22], help in selecting significant features based on domain relevance. Transforming omics datasets into new representations, like graph embeddings or unsupervised dimension reduction methods, may reduce model interpretability as the biological context of extracted features could be lost [13; 17; 24]. Overfitting occurs when a model fits training data perfectly but requires improvement in testing or unseen data. Overfitting is one of the challenges of using machine learning in drug response prediction [39]. Recent studies have employed various strategies to combat overfitting. Regularization techniques include Lasso (Least Absolute Shrinkage and Selection Operator) and Ridge Regression. For example, Lasso regression adds an L1 penalty to the loss function, leading to some coefficients becoming zero, which performs as a feature selection [40]. On the other hand, Ridge Regression shrinks the fitting coefficients using an L\({}_{2}\) penalty so that they have less variance and control the overfitting of the data. Nevertheless, it does not push the coefficients of each feature to be zero [41]. Ensemble methods, such as Gradient Boosting Machines (GBMs) and random forests, have proven effective in controlling overfitting and improving prediction accuracy [42]. GBMs build models sequentially to correct errors made by previous models, while random forests aggregate predictions of several weak learners, such as decision trees, and learn from various data samples, thereby averting overfitting and enhancing prediction [42]. In deep learning, dropout is a critical technique to prevent overfitting [43]. This method randomly sets a fraction of the input neurons to zero during training, effectively \"dropping out\" some neurons. Thus, any contribution they would have produced in a backward or forward pass while training is prohibited. Therefore, the model training occurs with noise, enabling the model to learn more robust and generalizable features, thereby reducing overfitting. Additionally, cross-validation is an essential stage often employed to assess how well a model conducts with different datasets, assuring that the model is generalized [39]. In the context of drug response prediction, [PERSON] et al. [44] and [PERSON] et al. utilized the early stopping technique to prevent overfitting. In this technique, model training halts when performance improvements in specific iterations of model validation are not observed [45]. They also employed various approaches to mitigate overfitting.