Article

sCellST: a Multiple Instance Learning approach to predict single-cell gene expression from H&E images using spatial transcriptomics

2024-11-08

Abstract excerpt

Advancing our understanding of tissue organization and its disruptions in disease remains a key focus in biomedical research. Histological slides stained with Hematoxylin and Eosin (H&E) provide an abundant source of morphological information, while Spatial Transcriptomics (ST) enables detailed, spatiallyresolved gene expression (GE) analysis, though at a high cost and with limited clinical accessibility. Predicti...

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Literature Corpus work: 29ade059-04dc-5ee3-86c7-8d93d9e9a65e
DOI: 10.1101/2024.11.07.622225

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