PGST: A Prototype-Guided Parameter-Efficient Network for Spatial Transcriptomics Prediction

Yuan He

doi:10.1109/jbhi.2026.3666148

2026-02-17

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DOI：10.1109/jbhi.2026.3666148

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摘要

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空间转录组学（ST）旨在解码空间分辨的基因表达模式，同时保留组织形态。当前方法倾向于使用低成本深度学习模型进行基因表达预测，但面临严峻挑战。首先，现有方法未能充分关注ST中固有的位置编码的空间特异性；其次，它们忽略了跨不同域的空间一致共表达模式；第三，其对线性加权聚类的依赖导致对噪声和分布漂移的脆弱性；最后，这些架构表现出有限的参数效率。为解决这些问题，我们提出空间转录组学的原型引导网络（PGST），包括四个部分：（1）通过极坐标嵌入策略实现定向信号传播的空间转录组学（PEST）；（2）原型引导的聚合以保留全局共特征；（3）通过共享解码器与重建损失实现全局一致性约束；（4）轻量化架构设计。我们的框架整合对比学习与图神经网络，以平衡局部-全局空间依赖和跨模态一致性。在多个ST数据集上的实验结果表明，我们的PGST模型优于现有方法。

Keyword:

Spatial transcriptomics

deep learning

histopathological images

graph neural network

期刊

IEEE Journal of Biomedical and Health Informatics

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6.8

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4.7K

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2.0W

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tianjin university

学者数:

8.0W

论文数: 5.8W

被引数: 88

CSIRO Data61

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论文数: 39

被引数: 0

Macao Polytechnic University

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1.6K

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被引数: 805

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