ReviewBriefings in bioinformatics2026
Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.
Review in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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10 authors.
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Abstract
Spatial omics (SO) enables spatially resolved molecular profiling, while hematoxylin and eosin (H&E) imaging remains the gold standard for morphological assessment in clinical pathology. Recent computational advances increasingly center H&E images in SO analysis and push resolution toward the single-cell level. We systematically review the computational evolution of SO from a histopathology-centered perspective, organizing methods into three paradigms: integration (jointly modeling of paired multimodal data), mapping (inferring molecular profiles from H&E images), and foundation models (learning generalizable representations from large-scale datasets). We summarize actionable modeling directions and persistent gaps, providing a roadmap for developing, and applying computational frameworks in SO.
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