Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review

Nasim Vahabi, George Michailidis
2022 Frontiers in Genetics  
Through the developments of Omics technologies and dissemination of large-scale datasets, such as those from The Cancer Genome Atlas, Alzheimer's Disease Neuroimaging Initiative, and Genotype-Tissue Expression, it is becoming increasingly possible to study complex biological processes and disease mechanisms more holistically. However, to obtain a comprehensive view of these complex systems, it is crucial to integrate data across various Omics modalities, and also leverage external knowledge
more » ... lable in biological databases. This review aims to provide an overview of multi-Omics data integration methods with different statistical approaches, focusing on unsupervised learning tasks, including disease onset prediction, biomarker discovery, disease subtyping, module discovery, and network/pathway analysis. We also briefly review feature selection methods, multi-Omics data sets, and resources/tools that constitute critical components for carrying out the integration.
doi:10.3389/fgene.2022.854752 pmid:35391796 pmcid:PMC8981526 fatcat:ijmwfu264rbgtaen66tkbr4sgu