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STAG: Biologically guided spatial transcriptomics prediction via hypergraph learning

delete2026-07-08
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OA
AI
M
Mingcheng Qu
Y
Yuchuan Zhao
G
Guang Yang
D
Donglin Di
X
Xiu Su
H
Hongyan Xu
Y
Yang Song
L
Lei Fan *
DOI：10.1016/j.media.2026.104206delete
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摘要

摘要

En 中文
• 基于超图的双分支框架被引入用于从组织学图像预测空间基因表达。• 该方法捕获组织学图像块与空间转录组学位点之间的上下文关系。• 建立了一个统一的评估协议，以确保基因范围和评估设置下PCC比较的公平性。• 在六个公开数据集上，所提出的方法在PCC@250上实现了5.16%的提升，下游癌症分类的AUC为0.748，伪3D设置下的位点级PCC为0.632。
Keyword:
Spatial transcriptomics
Whole-slide images
Cross-modal alignment
Contrastive learning
Hypergraph learning
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期刊

Medical Image Analysis 封面图
Medical Image Analysis
IF:
11.8
论文数:
3.8K
被引数:
2.4W

机构

C
central south university
学者数:
2.2W
论文数: 6.3K
被引数: 3
H
Harbin Institute of Technology
学者数:
1.5W
论文数: 4.9K
被引数: 8.5W
U
university of new south wales
学者数:
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论文数: 1.5K
被引数: 0
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