空间转录组学揭示了复杂组织中基因的空间分布，为生物过程、疾病机制和药物开发提供了重要的见解。基于具有成本效益的组织学图像的基因表达预测是一个有前途但具有挑战性的研究领域。现有的从组织学图像进行基因预测的方法存在两个主要局限性。首先，他们忽略了细胞形态信息和基因表达之间复杂的关系。其次，这些方法没有充分利用从图像中提取的特征的不同潜在阶段。为了解决这些限制，我们提出了一种新的超图神经网络模型 HGGEP，用于从组织学图像中预测基因表达。 HGGEP 包含梯度增强模块，用于增强模型对细胞形态信息的感知。轻量级骨干网络从图像中提取多个潜在阶段特征，然后通过注意机制来细化每个潜在阶段特征的表示并捕获它们与附近特征的关系。为了探索多个潜在阶段特征之间的高阶关联，我们将它们堆叠起来并输入超图以建立不同尺度特征之间的关联。对癌症和肿瘤疾病等疾病样本的多个数据集进行的实验结果证明，我们的 HGGEP 模型比现有方法具有优越的性能。© 作者 2024。由牛津大学出版社出版。

Spatial transcriptomics reveals the spatial distribution of genes in complex tissues, providing crucial insights into biological processes, disease mechanisms, and drug development. The prediction of gene expression based on cost-effective histology images is a promising yet challenging field of research. Existing methods for gene prediction from histology images exhibit two major limitations. First, they ignore the intricate relationship between cell morphological information and gene expression. Second, these methods do not fully utilize the different latent stages of features extracted from the images. To address these limitations, we propose a novel hypergraph neural network model, HGGEP, to predict gene expressions from histology images. HGGEP includes a gradient enhancement module to enhance the model's perception of cell morphological information. A lightweight backbone network extracts multiple latent stage features from the image, followed by attention mechanisms to refine the representation of features at each latent stage and capture their relations with nearby features. To explore higher-order associations among multiple latent stage features, we stack them and feed into the hypergraph to establish associations among features at different scales. Experimental results on multiple datasets from disease samples including cancers and tumor disease, demonstrate the superior performance of our HGGEP model than existing methods.© The Author(s) 2024. Published by Oxford University Press.