ArticleCell reports methods2025
SuperGLUE facilitates an explainable training framework for multi-modal data analysis.
Article in Cell reports methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
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3 authors.
Funding
Abstract
Single-cell multi-modal data integration has been an area of active research in recent years. However, it is difficult to unify the integration process of different omics in a pipeline and evaluate the contributions of data integration. In this article, we revisit the definition and contributions of multi-modal data integration and propose a strong and scalable method based on probabilistic deep learning with an explainable framework powered by statistical modeling to extract meaningful information after data integration. Our proposed method is capable of integrating different types of omics and sensing data. It offers an approach to discovering important relationships among biological features or cell states. We demonstrate that our method outperforms other baseline models in preserving both local and global structures and perform a comprehensive analysis for mining structural relationships in complex biological systems, including inference of gene regulatory networks, extraction of significant biological linkages, and analysis of differentially regulatory relationships.
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Registered trials
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