A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches
Ana R Baião, Zhaoxiang Cai, Rebecca C Poulos, Phillip J Robinson, Roger R Reddel, Qing Zhong, Susana Vinga, Emanuel Gonçalves
Abstract
The integration of multi-omics data is essential for understanding complex biological systems. This review provides a comprehensive overview of multi-omics data integration methods, ranging from classical statistical approaches to state-of-the-art deep generative models. We discuss the challenges associated with high dimensionality, heterogeneity, and missing data, and highlight the potential of Variational Autoencoders (VAEs) and other deep learning techniques for data imputation, augmentation, and joint embedding. The review also covers emerging trends such as foundation models and contrastive learning in the context of multi-omics integration.
BibTeX
@article{baião2025baiao2025review,
title = {{A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches}},
author = {Ana R Baião and Zhaoxiang Cai and Rebecca C Poulos and Phillip J Robinson and Roger R Reddel and Qing Zhong and Susana Vinga and Emanuel Gonçalves},
journal = {Briefings in Bioinformatics},
year = {2025},
doi = {10.1093/bib/bbaf355}
}