由查询驱动的生成人工智能从组织学中合成了多模式空间奥米克
Minxing Pang1, Tarun Kanti Roy2, Xiaodong Wu3
1Applied Mathematics & Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA, USA.
bioRxiv : the preprint server for biology
|December 19, 2025
概括
TissueCraftAI使用生成性AI从组织学图像中预测空间奥米克,弥合了病理学和分子数据之间的差距. 这种计算病理学工具增强了细胞注释和患者生存预测.
科学领域:
- 计算病理学计算病理学
- 医学中的人工智能.
- 空间奥米克斯 空间奥米克斯
背景情况:
- 空间奥米克提供了组织细胞组织洞察力,但缺乏临床可扩展性.
- 组织学染色图像是诊断的基础,但缺乏分子数据.
- 在仅仅从组织学推断分子数据时存在计算差距.
研究的目的:
- 介绍TissueCraftAI,一种生成性AI框架,可以从组织学图像中预测多模式空间奥米克图.
- 开发一种方法来弥合标准组织学和分子数据之间的差距.
- 启用查询驱动的in silico空间分子分析.
主要方法:
- 开发了TissueCraftAI,这是一个生成的人工智能框架.
- 创建了PRISM-12M,一个大规模的数据集,包含1200万个组织学和空间奥米克图像补丁.
- 利用自然语言提示来预测空间奥米克数据.
主要成果:
- 在生成现实的组织学图像方面,TissueCraftAI的性能优于现有的方法.
- 准确地预测空间蛋白质组学和转录组学数据的高保真度.
- 在改善细胞类型注释和患者生存预测方面证明有用.
结论:
- TissueCraftAI能够从常规组织学图像中准确地推断空间分子数据.
- 开辟了计算病理学和精准医学领域的新研究途径.
- 促进灵活的,查询驱动的in silico空间分子分析.
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