用于空间转录组学的双曲对比学习与蕴含关系
Hyperbolic Contrastive Learning with Entailment for Spatial Transcriptomics
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中文总结 AI 辅助
针对空间转录组学预测中的过度平滑问题，提出双曲对比学习模型HyCLoST，利用双曲几何和蕴含损失捕捉层次结构，在26个数据集上实现MSE降低6%、PCC提升8%。
中文摘要 AI 辅助
空间转录组学（ST）通过实现基因表达在组织切片上的空间映射，彻底改变了生物医学研究。然而，高昂的运营成本、专业设备要求以及对实验噪声的敏感性限制了ST的可及性和可扩展性。最近的计算机视觉方法旨在通过直接从组织病理学图像预测空间基因表达来克服这些限制。尽管这些方法有效，但当前的方法常常遭受基因表达过度平滑和跨组织区域预测过于均匀的问题，这表明进一步的进展取决于学习能够反映基因调控和组织形态学的层次性和不对称结构的表示。为了解决这些问题，我们提出了用于空间转录组学的双曲对比学习与蕴含关系（HyCLoST），这是一种双曲对比学习模型，能够捕捉ST数据中内在的层次关系。通过利用双曲几何和基因到图像的蕴含损失，HyCLoST学习到结构化、具有生物学基础的表示，从而提高了基因表达预测的准确性，在26个ST数据集上，与先前方法相比，均方误差（MSE）降低了6%，皮尔逊相关系数（PCC）提高了8%。我们的源代码可在以下网址公开获取：此https URL。
英文摘要
Spatial Transcriptomics (ST) has transformed biomedical research by enabling the spatial mapping of gene expression across tissue sections. However, high operational costs, specialized equipment requirements, and sensitivity to experimental noise limit the accessibility and scalability of ST. Recent computer vision approaches aim to overcome these limitations by predicting spatial gene expression directly from histopathology images. While effective, current approaches often suffer from gene expression over-smoothing and overly uniform predictions across tissue regions, suggesting that further progress depends on learning representations that reflect the hierarchical and asymmetric structure of gene regulation and tissue morphology. To address these issues, we propose Hyperbolic Contrastive Learning with Entailment for Spatial Transcriptomics (HyCLoST), a hyperbolic contrastive learning model that captures the intrinsic hierarchical relationships within ST data. By leveraging hyperbolic geometry and a gene-to-image entailment loss, HyCLoST learns structured, biologically grounded representations that improve gene expression prediction accuracy, achieving a 6% reduction in MSE and an 8% increase in PCC across 26 ST datasets, over previous methods. Our source code is publicly available at https://github.com/BCV-Uniandes/HyCLoST
发表机构
- Center for Research and Formation in Artificial Intelligence Universidad de los Andes, Colombia（安第斯大学人工智能研究与培养中心）
机构由 AI 辅助整理，请以论文原文为准。
