综述图神经网络在多组学癌症研究中的应用与趋势
Graph Neural Networks in Multi-Omics Cancer Research: A Structured Survey
- 按组学层级、模型结构和生物任务分类，梳理GNN在癌症研究中的方法
- 发现混合模型与可解释性设计成主流，注意力机制和对比学习广泛应用
- 适合生物信息学与人工智能交叉研究者参考，关注患者特异性图谱新方向
多组学数据整合已成为揭示癌症复杂生物学机制的有力策略。图神经网络（GNN）为建模异构且具有结构的组学数据提供了有效框架，能够精确表征分子互作与调控网络。本文系统综述了近年利用GNN架构进行多组学癌症研究的多项工作，按目标组学层级、图神经网络结构及生物任务（如亚型分类、预后预测、生物标志物发现）对方法进行分类。分析显示，混合模型与可解释性设计趋势明显，注意力机制与对比学习被广泛采用。此外，患者特异性图谱与知识驱动先验作为新兴方向受到关注。本综述为构建有效的多组学癌症分析GNN流程提供全面资源，揭示当前实践、局限与未来方向。
原文摘要 · Abstract (English)
The task of data integration for multi-omics data has emerged as a powerful strategy to unravel the complex biological underpinnings of cancer. Recent advancements in graph neural networks (GNNs) offer an effective framework to model heterogeneous and structured omics data, enabling precise representation of molecular interactions and regulatory networks. This systematic review explores several recent studies that leverage GNN-based architectures in multi-omics cancer research. We classify the approaches based on their targeted omics layers, graph neural network structures, and biological tasks such as subtype classification, prognosis prediction, and biomarker discovery. The analysis reveals a growing trend toward hybrid and interpretable models, alongside increasing adoption of attention mechanisms and contrastive learning. Furthermore, we highlight the use of patient-specific graphs and knowledge-driven priors as emerging directions. This survey serves as a comprehensive resource for researchers aiming to design effective GNN-based pipelines for integrative cancer analysis, offering insights into current practices, limitations, and potential future directions.
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