Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review.

ABSTRACT: 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 available 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 netw

SUBMITTER: Vahabi N 

PROVIDER: S-EPMC8981526 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature