Multi-omics integration and batch correction using a modality-agnostic deep learning framework.

ABSTRACT: State-of-the-art biotechnologies allow the detection of different molecular species on the same biological sample, generating complex highly-dimensional multi-modal datasets. Gaining a holistic understanding of biological phenomena, such as oncogenesis or aging, requires integrating these diverse modalities into low-dimensional data representations while correcting for technical artifacts. Here we present MIMA, a modular, unsupervised AI framework for multi-omics data integration and batch correction. Applied to complex spatial and single-cell datasets, MIMA effectively removes batch effects, while preserving biologically relevant information, and learns representations predictive of expert pathologist annotations. Additionally, it enables cross-modal translation, uncovers molecular patter

SUBMITTER: Alvira Larizgoitia JI 

PROVIDER: S-EPMC12633452 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature