ArticleNature communications2026
SMART: spatial multi-omic aggregation using graph neural networks and metric learning.
Article in Nature communications, 2026. 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- MultiSpaceNet: graph-based joint representation learning for paired spatial transcriptome-proteome data.Bioinformatics (Oxford, England) · 2026Article
- Decoding apoptosis, ferroptosis, and inflammatory cell death in adenomyosis at single-cell resolution.Briefings in bioinformatics · 2026Article
- Kinemomics: spatiotemporal morphodynamic mapping of ventricular kinematic subpopulations in organotypic fetal heart slices.bioRxiv : the preprint server for biology · 2026Article
- SMART: spatial multi-omic aggregation using graph neural networks and metric learning.Nature communications · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Spatial multi-omics enables the exploration of tissue microenvironments and heterogeneity from the perspective of different omics modalities across distinct spatial domains within tissues. To jointly analyze the spatial multi-omics data, computational methods are desired to integrate multiple omics with spatial information into a unified space. Here, we present SMART (Spatial Multi-omic Aggregation using gRaph neural networks and meTric learning), a computational framework for spatial multi-omic integration. SMART leverages a modality-independent modular and stacking framework with spatial coordinates and adjusts the aggregation using triplet relationships. SMART excels at accurately identifying spatial regions of anatomical structures, compatible with spatial datasets of any type and number of omics layers, while demonstrating exceptional computational efficiency and scalability on large datasets. Moreover, a variant of SMART, SMART-MS, expands its capabilities to integrate spatial multi-omics data across multiple tissue sections. In summary, SMART provides a versatile, efficient, and scalable solution for integrating spatial multi-omics data.
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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.