A multi-omics integration framework using multi-label guided learning and multi-scale fusion.

ABSTRACT: The rapid development of high-throughput sequencing technologies has generated vast amounts of omics data, making multi-omics integration a crucial approach for understanding complex diseases. Despite the introduction of various multi-omics integration methods in recent years, existing approaches still have limitations, primarily in their reliance on manual feature selection, restricted applicability, and inability to comprehensively capture both inter-sample and cross-omics interactions. To address these challenges, we propose mmMOI, an end-to-end multi-omics integration framework that incorporates multi-label guided learning and multi-scale attention fusion. mmMOI directly processes raw high-dimensional omics data without requiring manual feature selection, thereby enhancing model interp

SUBMITTER: Li Y 

PROVIDER: S-EPMC12461718 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature