Evaluating the ability of spatial transcriptomics foundation models to learn multi-scale spatial variation

Abstract excerpt

Spatial gene expression results from the superposition of multiple sources of variation in gene expression across different spatial scales, including local microenvironment-associated variation and global spatial gradients. Spatial foundation models (SFMs) are large-scale machine learning models trained on cohorts of spatial transcriptomics (ST) data that, in principle, learn the different sources of spatial varia...

Topics

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• Biomarkers, Tumor
• Gene Expression
• Gene Expression Profiling
• Image Processing, Computer-Assisted
• Machine Learning
• Neoplasms
• Phenotype
• Transcriptome

Identifiers and source

Literature Corpus work

bf1e517b-9e53-599a-af37-ed5c62094865

DOI

10.64898/2026.08.01.742217