基于HSIC瓶颈正则化多视图图对比学习的组织学空间分辨基因表达预测
Spatially Resolved Gene Expression Prediction from Histology via Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization
AI总结：
针对现有基于组织学图像的ST基因表达预测忽略斑点空间依赖、难提取跨模态共性信息的问题，提出带HSIC瓶颈正则化的多视图图对比学习框架ST-GCHB，学习共享表征以提升预测效果。
摘要：
空间转录组学（ST）的快速发展使得在空间分辨率下测量基因表达成为可能，从而能够同时分析基因表达、斑点的空间位置以及匹配的组织病理学图像。然而，收集ST数据的成本远高于获取组织病理学图像，因此已有多项研究尝试利用对应的组织病理学图像来预测ST上的基因表达。大多数现有的基于图像的基因预测模型将ST数据每个斑点上的预测任务视为独立的，忽略了斑点之间的空间依赖性。此外，尽管组织学图像与ST数据具有表型特征共性，但提取此类共同信息以帮助对齐配对的图像与表达表征仍具有挑战性。为解决上述问题，我们提出一种带HSIC瓶颈正则化的多视图图对比学习框架（ST-GCHB），旨在学习共享表征，通过考虑空间依赖性来填补查询成像斑点的基因表达。
英文摘要：
The rapid development of spatial transcriptomics(ST) enables the measurement of gene expression at spatial resolution, making it possible to simultaneously profile the gene expression, spatial locations of spots, and the matched histopathological images. However, the cost for collecting ST data is much higher than acquiring histopathological images, and thus several studies attempt to predict the gene expression on ST by leveraging their corresponding histopathological images. Most of the existing image-based gene prediction models treat the prediction task on each spot of ST data independently, which ignores the spatial dependency among spots. In addition, while the histology images share phenotypic characteristics with the ST data, it is still challenge to extract such common information to help align paired image and expression representations. To address the above issues, we propose a Multi-view Graph Contrastive Learning framework with HSIC-bottleneck Regularization(ST-GCHB) aiming at learning shared representation to help impute the gene expression of the queried imagingspots by considering their spatial dependency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
