ARO: Aligned Representation learning for multi-Omics data

2026-10-05

AI Summary

This study addresses critical bottlenecks in cancer multi-omics analysis, including missing data, unmatched samples, and high assay costs, by proposing the ARO model. Leveraging representation learning and multimodal alignment techniques, ARO constructs a unified representation space for multi-omics data. Unlike large foundation models that require massive datasets, ARO prioritizes clinical practicality, enabling high-fidelity reconstruction of missing modalities and robust downstream classification. Experimental results demonstrate that the model achieves a mean squared error (MSE) of 0.15 on the validation set, effectively facilitating multi-omics integrative inference under data-constrained scenarios. By significantly reducing reliance on large-scale experimental profiling, this work presents an efficient and practical paradigm for incomplete multi-omics analysis.

Abstract

The high cost of functional molecular assays, and prevalence of missing modalities and unmatched samples in computational biology, create significant barriers to comprehensive multi-omic profiling, essential for capturing and reasoning over molecules, cells, tissues, and organisms. This work proposes a model that learns meaningful representations from multi-omics cancer data supporting the reconstruction of missing and unpaired modalities. Contrary to increasingly complex, larger models, e.g. Foundation Models (FMs), ARO prioritizes practical applicability in limited or incomplete data settings. ARO optimally reconstructs missing modalities (MSE of $0.15$ on the validation and test data in the Unmasked settings), with its learned latent embeddings enabling a downstream cancer classification task. Our findings indicate that analyzing diverse molecular layers as a single integrated system offers a reliable and cost-efficient approach, reducing dependence on large-scale experimental testing, while still supporting multi-omic exploration in limited data settings.

Problem

• multi-omics data
• missing modalities
• representation learning
• computational biology

Innovation

• Multi-omics
• Representation Learning
• Missing Modality Reconstruction
• Aligned Embeddings
• Cancer Classification