ArticleNature communications2025
Graph neural networks learn emergent tissue properties from spatial molecular profiles.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- SpaCEy links spatial tissue patterns to clinical outcomes using explainable graph neural networks.Nature communications · 2026Article
- Semi-metric optimal transport enables robust spatial multi-omics integration.Research square · 2026Article
- Lineage Classification of Pituitary Neuroendocrine Tumors From Whole-Slide Images Using Attention-Guided Graph Representation Learning.Endocrine pathology · 2026Article
- Decoupling Size from Shape: Cellular Sheaf Laplacians as Ligand Geometry Descriptors for Binding Affinity Prediction.International journal of molecular sciences · 2026Article
- MICRON learns outcome-associated representations of spatial immune microenvironments.bioRxiv : the preprint server for biology · 2026Article
- Deciphering immunosuppressive niches by spatial single-cell proteomics: spatial interaction networks and translational opportunities.Frontiers in immunology · 2026Review
- Editorial: Artificial intelligence in multi-omics: advancing tumor metastasis prediction and mechanism analysis.Frontiers in cell and developmental biology · 2026Article
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- Plying potency assays for immunotherapy of solid tumors.Frontiers in immunology · 2026Review
- A Novel Integrative Framework for Depression: Combining Network Pharmacology, Artificial Intelligence, and Multi-Omics with a Focus on the Microbiota-Gut-Brain Axis.Current issues in molecular biology · 2025Review
- Decrypting cancer's spatial code: from single cells to tissue niches.Molecular oncology · 2025Review
- Graph neural networks learn emergent tissue properties from spatial molecular profiles.Nature communications · 2025Article
- Discovery and generalization of tissue structures from spatial omics data.Cell reports methods · 2024Article
- GraphCompass: spatial metrics for differential analyses of cell organization across conditions.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Tissue phenotypes, such as metabolic states, inflammation, and tumor properties, emerge from both molecular states and spatial cell organization. Spatial molecular assays provide an unbiased view of tissue architecture, enabling phenotype prediction. Graph neural networks (GNNs) offer a natural framework for analyzing spatial proteomics by integrating expression profiles with structure. We apply GNNs to classify tissue phenotypes using spatial cell patterns. We show that for relatively simple classification tasks, such as tumor grading in breast cancer, incorporating spatial context does not significantly improve predictive performance over models trained on single-cell or pseudobulk representations. However, GNNs capture meaningful spatial features, retaining prognostic signals beyond tumor labels, highlighting tumor-grade-specific cell type interactions, and uncovering complex immune infiltration patterns in colorectal cancer not detectable with traditional approaches. These findings suggest that while spatial dependencies may not always enhance classification performance in small datasets, GNNs remain valuable tools for characterizing tissue organization and interactions.
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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.