ArticleCommunications biology2025
Unveiling spatial domains from spatial multi-omics data using dual-graph regularized ensemble learning.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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Who cites it
4 citing papers in PubMed.
- Decoding the breast cancer microenvironment by spatial multi-omics: from architecture to clinical translation.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- SpaBalance: Balanced Learning for Efficient Spatial Multi-Omics Decoding.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Spatial transcriptomics meets diabetic kidney disease: Illuminating the path to precision medicine.World journal of diabetes · 2025Review
- Unveiling spatial domains from spatial multi-omics data using dual-graph regularized ensemble learning.Communications biology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
With the rapid advancement of spatial multi-omics technologies, the simultaneous analysis of molecular profiles and spatial locations has provided unprecedented insights into cellular heterogeneity and tissue microenvironments. However, data sparsity and the diversity of data distributions hinder the effective integration and analysis of spatial multi-omics data. In this study, we propose a novel ensemble learning framework based on dual-graph regularized anchor concept factorization, named SMODEL, for detecting spatial domains from spatial multi-omics data. SMODEL employs an element-wise weighted ensemble strategy to integrate multiple base clustering results, and leverages anchor concept factorization and dual-graph regularization to learn robust spatial consensus representations. We evaluated the performance of SMODEL on both real and simulated spatial multi-omics datasets, encompassing various technologies, tissue types, and species. Experimental results demonstrate that SMODEL not only outperforms existing methods in spatial domain identification but also effectively captures tissue structure, thereby enhancing the understanding of cellular heterogeneity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.