Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review

53Citations
Citations of this article
165Readers
Mendeley users who have this article in their library.
Get full text

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 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.

Cite

CITATION STYLE

APA

Vahabi, N., & Michailidis, G. (2022, March 22). Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review. Frontiers in Genetics. Frontiers Media S.A. https://doi.org/10.3389/fgene.2022.854752

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free