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

Abstract excerpt

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...

Topics

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• Single-cell and spatial transcriptomics
• Bioinformatics and Genomic Networks
• Cancer Genomics and Diagnostics
• Chromatin
• Gene expression and cancer classification
• Phenotype
• Radiomics and Machine Learning in Medical Imaging
• Transcriptome

Identifiers and source

Literature Corpus work

42940608-2c4e-5401-97b6-e582e83b7b50

DOI

10.1101/2025.10.21.683449