ArticleResearch (Washington, D.C.)2025
Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile Analysis.
Article in Research (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- A Generalizable Multimodal Model for Treatment-Stratified Risk and Survival Assessment under Real-World Constraints: A Multi-Center Study of Colorectal Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Current Advances in Spatial Pathology of Nasal Polyps: Challenges, Opportunities for Artificial Intelligence, and Future Directions.World journal of otorhinolaryngology - head and neck surgery · 2026Review
- Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.Briefings in bioinformatics · 2026Review
- Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2026Article
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Genomic Characterization of Lung Cancer in Never-Smokers Using Deep Learning.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2026Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Iron Oxide-Based Surface-Enhanced Raman Spectroscopy Bioprobes for Detecting Tumor Cells in Serous Effusion.Analytical chemistry · 2026Article
- Cell Segmentation as Strategic Decision Making.Research (Washington, D.C.) · 2026Article
- MRI-Based Deep Learning Guides Multi-Omics Discovery of NBPF4 as a Therapeutic Target for Breast Cancer Lymph Node Metastasis.Research (Washington, D.C.) · 2026Article
- Longitudinal Characterization of GiantResearch (Washington, D.C.) · 2026Article
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- Doping-Engineered Proangiogenic Nanozymes Orchestrate Ischemic Tissue Regeneration via Cytoprotection and Revascularization.Research (Washington, D.C.) · 2026Article
- Artificial Intelligence in Histopathological Analysis for Predicting Immunotherapy Response in Cutaneous Melanoma.International journal of molecular sciences · 2025Review
- Transformative advances in single-cell omics: a comprehensive review of foundation models, multimodal integration and computational ecosystems.Journal of translational medicine · 2025Review
- Computer Vision Methods for Spatial Transcriptomics: A Survey.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Spatially resolved transcriptomics enable comprehensive measurement of gene expression at subcellular resolution while preserving the spatial context of the tissue microenvironment. While deep learning has shown promise in analyzing SCST datasets, most efforts have focused on sequence data and spatial localization, with limited emphasis on leveraging rich histopathological insights from staining images. We introduce GIST, a deep learning-enabled gene expression and histology integration for spatial cellular profiling. GIST employs histopathology foundation models pretrained on millions of histology images to enhance feature extraction and a hybrid graph transformer model to integrate them with transcriptome features. Validated with datasets from human lung, breast, and colorectal cancers, GIST effectively reveals spatial domains and substantially improves the accuracy of segmenting the microenvironment after denoising transcriptomics data. This enhancement enables more accurate gene expression analysis and aids in identifying prognostic marker genes, outperforming state-of-the-art deep learning methods with a total improvement of up to 49.72%. GIST provides a generalizable framework for integrating histology with spatial transcriptome analysis, revealing novel insights into spatial organization and functional dynamics.
Identifiers
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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.