一个视觉-omics基础模型,以桥梁 histopathology 图像与转录学
Weiqing Chen1,2, Pengzhi Zhang1,3,4,5, Tu N Tran1,3,4,5
1Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, TX, 77030, USA.
Research square
|May 5, 2025
概括
我们开发了OmiCLIP,这是一个视觉omics基础模型,用于整合组织组织学图像和转录学数据. 基于OmiCLIP构建的Loki平台,可以进行高级分析,在计算生物学任务中展示出卓越的准确性.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 基因组学中的人工智能
背景情况:
- 像单细胞RNA测序 (scRNA-seq) 和空间转录组学 (ST) 等omics技术可以生成详细的基因组数据.
- 现有的计算模型经常单独分析omics数据或组织学图像,阻碍了综合分析.
- 需要模型,可以有效地将组织学特征与转录基因概况联系起来.
研究的目的:
- 开发一种新的视觉-奥米基基础模型,OmiCLIP,用于将血素和素 (H&E) 图像与转录基因数据集成.
- 创建Loki平台,利用OmiCLIP,提供一套用于视觉omics分析的工具.
- 评估与最先进的模型对比Loki平台的性能.
主要方法:
- 开发了OmiCLIP,这是一个基础模型,基于220万张配对组织图像和32个器官的转录基因数据进行训练.
- 将转录基因数据转化为"句子"格式,通过连接每个组织补丁上表达的顶部基因符号.
- 构建了Loki平台的功能,包括组织对齐,注释,细胞类型分解,检索和ST基因表达预测.
主要成果:
- OmiCLIP成功地整合了组织学和转录学数据.
- 洛基平台在各种数据集中表现出一致的准确性和稳定性.
- 洛基在模拟和实验数据集的比较分析中表现优于22个最先进的模型.
结论:
- 通过弥合图像和omics数据之间的差距,OmiCLIP代表了视觉omics的重大进步.
- 洛基平台为计算生物学和相关领域的研究人员提供了一个强大而通用的工具.
- 综合的视觉-omics分析具有更深层次的生物学洞察力的巨大潜力.
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