ArticleiScience2025
Dissection of tumoral niches using spatial transcriptomics and deep learning.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- From descriptive to generative: foundation-model approaches for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- 3D chromatin architecture-related genes orchestrate LUAD evolution and therapy resistance: insights from integrative machine learning and spatial single-cell mapping.Functional & integrative genomics · 2026Article
- Integrating AI, RNA Vaccines, and CAR-T Cells for Personalized Treatment.Journal of immunology research · 2026Review
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- Deciphering spatially confined immune evasion niches in osteosarcoma with 3-D spatial transcriptomics: a literature review.Frontiers in oncology · 2025Review
- MiR-4664-3p as a potential diagnostic, prognostic, and immunotherapeutic biomarker in NSCLC: modulation of tumor progression through CD8 + T cell regulation.Frontiers in oncology · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
This study introduces TG-ME, an innovative computational framework that integrates transformer with graph variational autoencoder (GraphVAE) models for dissection of tumoral niches using spatial transcriptomics data and morphological images. TG-ME effectively identifies and characterizes niches in bench datasets and a high resolution NSCLC dataset. The pipeline consists in different stages that include normalization, spatial information integration, morphological feature extraction, gene expression quantification, single cell expression characterization, and tumor niche characterization. For this, TG-ME leverages advanced deep learning techniques that achieve robust clustering and profiling of niches across cancer stages. TG-ME can potentially provide insights into the spatial organization of tumor microenvironments (TME), highlighting specific niche compositions and their molecular changes along cancer progression. TG-ME is a promising tool for guiding personalized treatment strategies by uncovering microenvironmental signatures associated with disease prognosis and therapeutic outcomes.
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What OpenQuestion holds
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.