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Predicting Gene Spatial Expression and Cancer Prognosis: An Integrated Graph and Image Deep Learning Approach Based on HE Slides

2023-07-21

Abstract excerpt

<h4>ABSTRACT</h4> Interpreting the tumor microenvironment (TME) heterogeneity within solid tumors presents a cornerstone for precise disease diagnosis and prognosis. However, while spatial transcriptomics offers a wealth of data, ranging from gene expression and spatial location to corresponding Hematoxylin and Eosin (HE) images, to explore the TME of various cancers, its high cost and demanding infrastructural n...

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Literature Corpus work: 6440e9f7-054e-5dcb-aeff-90325119fafd
DOI: 10.1101/2023.07.20.549824

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