ArticleNPJ digital medicine2025
STPath: a generative foundation model for integrating spatial transcriptomics and whole-slide images.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- From descriptive to generative: foundation-model approaches for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Artificial Intelligence and Digital Pathology for Molecular Classification of Endometrial Cancer.International journal of molecular sciences · 2026Review
- Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.Briefings in bioinformatics · 2026Review
- Review
- HisCMCL: cross-modal contrastive learning with hierarchical multi-scale fusion for spatial expression prediction.Bioinformatics (Oxford, England) · 2026Article
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Mapping safety in space: the emerging role of spatial transcriptomics in safe drug development.Frontiers in toxicology · 2026Review
- Insights, opportunities, and challenges provided by large cell atlases.Genome biology · 2025Review
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial transcriptomics (ST) offers insights into gene expression patterns and their spatial context within the tumor microenvironment, but remains limited by the scalability of current sequencing technologies. Existing approaches infer ST from whole-slide images (WSIs) using pretrained encoders, yet are restricted by narrow gene coverage, organ-specific training, and dataset-specific fine-tuning. In light of this, we present STPath, a generative foundation model pretrained on large-scale WSIs paired with ST profiles. This extensive pretraining enables STPath to directly predict gene expression across 38,984 genes and 17 organs without downstream fine-tuning. STPath integrates histology images, gene expression, organ type, and sequencing technology modality within a geometry-aware Transformer, trained via masked gene expression prediction with tailored noise schedules to capture gene-gene dependencies and enable high-quality inference. Evaluated on six tasks spanning 23 datasets and 14 biomarkers, including expression prediction, spot imputation, spatial clustering, biomarker prediction, mutation prediction, and survival prediction, STPath demonstrates strong applicability for scalable ST-based pathology applications.
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.