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This research introduces ARO, a machine learning model designed to reconstruct missing data modalities in multi-omics cancer datasets, addressing the high costs and incompleteness typical of functional molecular assays. By prioritizing practical applicability over the scale of foundation models, the approach demonstrates that integrated analysis of diverse molecular layers can reliably support downstream tasks like cancer classification even with limited experimental data.
ARO: Aligned Representation learning for multi-Omics data
arXiv:2610.06443v1 Announce Type: cross 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.
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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2610.06443 [cs.LG]
(or arXiv:2610.06443v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.06443 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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