# Integrating Multi-Omics Using Bayesian Ridge Regression with Iterative Similarity Bagging
## 2 Related Work  
Several recent reviews have presented methods for integrating multi-omics data using statistical models, machine learning, or deep learning, either supervised or unsupervised [6; 7; 10; 12; 22; 32; 33]. This brief discussion will introduce some recent methods, focusing on integration strategies: early integration, mixed or middle integration, and late integration. For early integration, [PERSON] et al. [34] introduced iMO-BSPC (Integrative Analysis of Multi-omics Data Based on Blockwise Sparse Principal Components). The algorithm conducted variable clustering for each omics dataset. Their approach identified the initial sparse principal components (sPCs) as surrogate variables for dimensionality reduction for each cluster or block. The sPCs obtained from each omics data source were then combined to create a unified multi-omics dataset. [PERSON] et al. [35] presented a novel approach called GDP (Group Lasso Regularized Deep Learning for Cancer Prognosis) to analyze survival. This method leverages gene-level group prior knowledge. GDP was employed to integrate clinical data with other types of molecular data, including RNA-seq, copy number variation (CNV), normalized RPPA protein expression data, and DNA somatic mutation data. The GPD technique was utilized to regularize the neural network's input layer coefficients. In their study, \"group prior knowledge\" refers to the ability to combine many characteristics of the same gene, such as copy number variation, gene expression level, protein expression level, and single-nucleotide polymorphism, during regularization. For graph-based methods, a mixed integration approach has been considered. [PERSON] et al. [36] developed Learning Graph Representation for Drug Response Prediction (LGRDRP) to predict cell line drug responses. LGRDRP first builds a heterogeneous network that integrates cell line miRNA expression profiles, drug chemical structural similarity, gene-gene interaction, known cell line drug responses, and cell line-gene interaction. The learning graph representation and Laplacian feature selection are then combined for each cell line to obtain network topology features related to the cell line. The learning graph representation method learns network topology structural features, and the Laplacian feature selection works to choose the most informative features. [PERSON] et al. [17] presented a multi-omics data integration approach that utilizes random walk with restart (RWR) over a multiplex network. The resulting process is a Random Walk with Restart for Multi-dimensional Data Fusion (RWRF). RWRF utilizes the similarity network of samples as the basis framework for integration. The process involves creating a similarity network for each data type and linking the appropriate samples from numerous similarity networks to form a multiplex sample network. RWRF uses the stationary probability distribution to combine similarity networks by implementing RWR on the multiplex network. Graph-embedding approaches were introduced to integrate multi-omics by extracting new features representing each \"omic\". [PERSON] et al. [13] proposed DITGBDT, a gradient boosting decision tree-based drug-target interaction prediction approach. They created a drug-target heterogeneous network with drug similarities based on chemical structures, target sequence similarities, and known drug-target interactions. Random walks updated drug or target similarities by capturing network topology. Multiple groups of drug-target paths were identified, and their features were extracted. To integrate multi-omics using kernel-based approaches for predicting breast cancer survival, [PERSON] et al. [19] utilized multiple kernel learning (MKL) to effectively integrate somatic mutation with contemporary molecular data such as methylation, copy number variation (CNV), gene expression, and protein expression. Maximum relevance minimum redundancy (mRMR) was implemented as a feature selection method to select informative features for each data type. For late integration, [PERSON] et al.