SDGCCA: Supervised Deep Generalized Canonical Correlation Analysis for Multi-Omics Integration.

ABSTRACT: Integration of multi-omics data provides opportunities for revealing biological mechanisms related to certain phenotypes. We propose a novel method of multi-omics integration called supervised deep generalized canonical correlation analysis (SDGCCA) for modeling correlation structures between nonlinear multi-omics manifolds that aims at improving the classification of phenotypes and revealing the biomarkers related to phenotypes. SDGCCA addresses the limitations of other canonical correlation analysis (CCA)-based models (such as deep CCA, deep generalized CCA) by considering complex/nonlinear cross-data correlations between multiple (≥2) modalities. Although there are a few methods to learn nonlinear CCA projections for classifying phenotypes, they only consider two views. Methods extended

SUBMITTER: Moon S 

PROVIDER: S-EPMC9805883 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature