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A perspective on developing foundation models for analyzing spatial transcriptomic data

delete2025-12-01
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OA
AI
T
Tianyu Liu
M
Minsheng Hao
X
Xinhao Liu
赵宏宇 cover
赵宏宇 (Hongyu Zhao) *
DOI:10.1002/qub2.70010delete
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Abstract

Abstract

En 中文
Do we need a foundation model (FM) for spatial transcriptomic analysis? To answer this question, we prepared this perspective as a primer. We first review the current progress of developing FMs for modeling spatial transcriptomic data and then discuss possible tasks that can be addressed by FMs. Finally, we explore future directions of developing such models for understanding spatial transcriptomics by describing both opportunities and challenges. In particular, we expect that a successful FM should boost research productivity, increase novel biological discoveries, and provide user-friendly access.
Keywords:
artificial intelligence
foundation models
perspective
spatial transcriptomics data
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Q
Quantitative Biology
IF:
1.4
Papers:
20
Citations:
0

Organization

Y
Yale University
Scholars:
6.5W
Papers: 6.0W
Citations: 10.0W
R
roche holding usa
Scholars:
3.4K
Papers: 2.0K
Citations: 2
R
roche holding
Scholars:
2.1W
Papers: 1.1W
Citations: 9
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