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bioRxiv · 10.1101/2025.06.16.659949

JASMINE: A powerful representation learning method for enhanced analysis of incomplete multi-omics data

Ballard, J. L.·Dai, Z.·Shen, L.·Long, Q.
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Abstract

Integrative analysis of multi-omics data provides a more comprehensive and nuanced view of a subjects biological state. However, high-dimensionality and ubiquitous modality missingness present significant analytical challenges. Existing methods for incomplete multi-omics data are scarce, do not fully leverage both modality-specific and shared information, and produce task-biased representations. We propose JASMINE, a self-supervised representation learning method for incomplete multi-omics data that preserves both modality-specific and joint information and enhances sample similarity structure. JASMINE produces embeddings that achieve superior performance across multiple tasks for two different incomplete multi-omics datasets while requiring only a single round of training per dataset.

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Ballard, J. L., Dai, Z., Shen, L., Long, Q.. 2025-06-22. JASMINE: A powerful representation learning method for enhanced analysis of incomplete multi-omics data. https://doi.org/10.1101/2025.06.16.659949

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