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Remics: a redescription-based framework for multi-omics analysis
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DOI：10.3389/fcell.2026.1738010.png)
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Abstract
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Complex diseases such as cancer are characterized by their intricate etiology; arising from several molecular mechanisms that span multiple omic layers. To obtain insights on disease subtypes; associated biomarkers; and improve prognostic modeling; it is essential to integrate and interpret multi-omics data in a biologically meaningful way. We introduce Remics; a redescription-based framework for multi-omics integration inspired by higher-order statistical representations. Remics leverages higher-order cumulants to identify redescriptions; which are sets of multi-omics features that jointly capture equivalent biological variation across modalities. These feature groups are further analyzed through network representations; multi-omics risk scoring; and biomarker discovery to reveal molecular interactions underlying disease mechanisms. We applied Remics on simulated data as well as multi-omics data of six different cancer types from The Cancer Genome Atlas. We demonstrate that redescription-based integration uncovers functionally coherent cross-omics feature associations and compare them with state-of-the-art approaches. Our results highlight the potential of higher-order multi-omics statistical analysis to advance precision medicine through improved interpretability and discovery of novel molecular relationships.
Keywords:
multi-omics
biomarker discovery
disease prediction
data mining
statistics
genetic epidemiology
networks
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