Feature selection and dimensionality reduction in multi-omics.

Authors

Sweta Rai, Igor Kovalchuk

Published in

Genome. Oct 18, 2026. Epub Oct 18, 2026.

Abstract

High-throughput multi-omics technologies generate high-dimensional, heterogeneous and noisy datasets, making robust biomarker discovery and disease modelling challenging. This review critically examines dimensionality reduction and representation strategies for multi-omics analysis while distinguishing three related but different goals: feature selection, feature extraction/representation learning, and multi-omics integration. We use sparse canonical correlation analysis (sCCA), variational autoencoders (VAEs), and graph convolutional networks (GCNs) as representative case studies spanning sparse linear, probabilistic deep-learning, and graph-based approaches. Rather than treating these methods as interchangeable, we compare their data requirements, assumptions, interpretability, ability to accommodate non-linearity and missing modalities, computational demands, and validation needs. Applications across cancer, cardiovascular and neurodegenerative disease, and across bulk, single-cell, spatial and imaging multi-omics are summarized in comparative tables. We conclude with practical guidance for matching method families to biological questions and emphasize that robust biomarker claims require leakage-free model development, stability assessment and independent validation.

PMID:
42893622
Bibliographic data and abstract were imported from PubMed on 19 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.