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SRLST: a unified multimodal representation learning framework for spatial transcriptomics analysis

delete2026-07-21
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OA
AI
W
Wei Lan
X
Xiao Deng
T
Tongsheng Ling
G
Guohang He
X
Xuhua Yan *
R
Ruiqing Zheng
黎
黎珉 (Min Li)
Shirui Pan cover
Shirui Pan (Shirui Pan)
Y
Yi Pan
DOI：10.1093/bioinformatics/btag524delete
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Abstract

Abstract

En 中文
Spatial transcriptomics (ST) enables molecular profiling within native tissue architecture, yet accurate delineation of spatial domains in ST data is challenging, as it demands the coordinated integration of transcriptomic, spatial, and tissue histological information.

Journal

Bioinformatics cover
Bioinformatics
IF:
5.4
Papers:
1.3K
Citations:
17.9W

Organization

G
Guangxi University
Scholars:
2.1K
Papers: 505
Citations: 0
C
Central South University
Scholars:
5.8K
Papers: 1.5K
Citations: 0
S
shenzhen university of advanced technology
Scholars:
115
Papers: 79
Citations: 0
G
Griffith University
Scholars:
412
Papers: 206
Citations: 0
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