从系统生物学视角出发，以基因组、蛋白质组与病理成像数据的层级融合推进癌症预后
Advancing Cancer Prognosis with Hierarchical Fusion of Genomic, Proteomic and Pathology Imaging Data from a Systems Biology Perspective
对做计算病理与多模态融合的研究者，这篇论文提供了把基因组、蛋白质组与病理图像按生物层级逐级融合的架构范式：用图感知交叉注意力建模基因-蛋白调控、用超图卷积建模蛋白-图像块的高阶关联，可作为多组学+全切片图像融合的可复用设计。
原文依据 we propose HFGPI, a hierarchical fusion framework that models the biological progression from genes to proteins to histology images
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论为提升癌症预后精度，近期研究日益关注通过整合基因组数据与组织学图像的多模态生存分析方法。然而，现有方法忽视了蛋白质组作为连接基因组改变与组织病理特征之间的中间层，同时提供了对生存预测至关重要的互补生物学信息这一事实。这一生物学现实还暴露出另一架构层面的局限：现有的整合分析研究以扁平方式融合这些异构数据源，无法刻画其固有的生物学层级。为解决上述局限，我们提出 HFGPI，一个从系统生物学视角对基因到蛋白质再到组织学图像的生物学进程进行建模的层级融合框架。具体而言，我们引入分子分词器（Molecular Tokenizer），一种分子编码策略，将身份嵌入与表达谱相结合，为基因和蛋白质构建具有生物学信息的表征。随后我们开发基因调控蛋白质融合（GRPF），采用图感知交叉注意力与结构保持对齐，显式建模基因-蛋白质调控关系并生成受基因调控的蛋白质表征。此外，我们提出蛋白质引导超图学习（PGHL），在蛋白质与图像块之间建立关联，利用超图卷积捕获高阶的蛋白质-形态学关系。最终特征在层级间逐步融合，以实现精确的生存结局预测。在五个基准数据集上的大量实验表明，HFGPI 优于最优方法。
英文原文摘要 1746 字符
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.