arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新，休息一下吧，周末愉快~~
arXiv 2603.13787cs.CV

从系统生物学视角出发，通过多层次融合基因组、蛋白质组和病理影像数据以推进癌症预后

Advancing Cancer Prognosis with Hierarchical Fusion of Genomic, Proteomic and Pathology Imaging Data from a Systems Biology Perspective

Junjie Zhou, Bao Xue, Meiling Wang, Wei Shao, Daoqiang Zhang

阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文提出HFGPI框架，通过分子编码策略和蛋白质引导超图学习，多层次融合基因组、蛋白质组和病理影像数据，提升癌症预后预测精度。

摘要：

为了提高癌症预后的准确性，最近的研究越来越多地关注多模态生存方法，通过整合基因组数据和组织学图像。然而，现有方法忽略了蛋白质组作为连接基因组改变和组织病理特征的中间层，同时提供对生存预测至关重要的互补生物学信息。这种生物学现实暴露了另一个架构限制：现有的整合分析研究以扁平方式融合这些异质数据源，无法捕捉其固有的生物层次结构。为了解决这些限制，我们提出了HFGPI，一种从系统生物学视角出发的层次融合框架，该框架从基因到蛋白质再到组织学图像建模生物进程。具体而言，我们引入了分子标记器，一种分子编码策略，通过整合身份嵌入与表达谱来构建生物信息导向的基因和蛋白质表示。然后，我们开发了基因调控蛋白质融合（GRPF），它利用图感知交叉注意力与结构保持对齐，以显式建模基因-蛋白质调控关系并生成基因调控蛋白质表示。此外，我们提出了蛋白质引导超图学习（PGHL），它在蛋白质和图像块之间建立关联，利用超图卷积捕捉更高阶的蛋白质-形态关系。最终特征在层次化层中逐步融合，以实现精确的生存结果预测。在五个基准数据集上的广泛实验表明，HFGPI优于最先进的方法。

英文摘要：

To enhance the precision of cancer prognosis, recent research has increasingly focused on multimodal survival methods by integrating genomic data and histology images. However, current approaches overlook the fact that the proteome serves as an intermediate layer bridging genomic alterations and histopathological features while providing complementary biological information essential for survival prediction. This biological reality exposes another architectural limitation: existing integrative analysis studies fuse these heterogeneous data sources in a flat manner that fails to capture their inherent biological hierarchy. To address these limitations, we propose HFGPI, a hierarchical fusion framework that models the biological progression from genes to proteins to histology images from a systems biology perspective. Specifically, we introduce Molecular Tokenizer, a molecular encoding strategy that integrates identity embeddings with expression profiles to construct biologically informed representations for genes and proteins. We then develop Gene-Regulated Protein Fusion (GRPF), which employs graph-aware cross-attention with structure-preserving alignment to explicitly model gene-protein regulatory relationships and generate gene-regulated protein representations. Additionally, we propose Protein-Guided Hypergraph Learning (PGHL), which establishes associations between proteins and image patches, leveraging hypergraph convolution to capture higher-order protein-morphology relationships. The final features are progressively fused across hierarchical layers to achieve precise survival outcome prediction. Extensive experiments on five benchmark datasets demonstrate the superiority of HFGPI over state-of-the-art methods.

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。

↑