通过图异常分析发现癌基因导致的边权异质性，提升癌症基因识别效果。
Rethinking Cancer Gene Identification through Graph Anomaly Analysis
- 从边权异质性出发，结合谱分析揭示癌基因在图中的异常模式。
- 在STRINGdb和CPDB数据集上，模型性能显著优于现有方法。
- 适合对生物网络分析与图神经网络交叉领域感兴趣的科研人员。
图神经网络（GNN）在整合蛋白质互作（PPI）网络识别癌症基因方面展现出潜力。然而，由于对PPI网络中生物信息建模不足，癌基因在图结构中复杂的互作模式仍缺乏深入探索。本研究首次将癌基因引起的生物互作异常与统计图异常相联系，发现癌基因节点表现出独特的图异常特征——边权异质性，即其连接边的权重方差显著更高。从谱视角看，该异质性会导致谱能量“平坦化”，能量集中于谱的两端。基于此，提出分层视角图神经网络HIPGNN，同时捕捉谱域的能量分布变化与空间域的蛋白互作上下文。在两个重处理数据集STRINGdb和CPDB上的大量实验表明，HIPGNN具有明显优势。
原文摘要 · Abstract (English)
Graph neural networks (GNNs) have shown promise in integrating protein-protein interaction (PPI) networks for identifying cancer genes in recent studies. However, due to the insufficient modeling of the biological information in PPI networks, more faithfully depiction of complex protein interaction patterns for cancer genes within the graph structure remains largely unexplored. This study takes a pioneering step toward bridging biological anomalies in protein interactions caused by cancer genes to statistical graph anomaly. We find a unique graph anomaly exhibited by cancer genes, namely weight heterogeneity, which manifests as significantly higher variance in edge weights of cancer gene nodes within the graph. Additionally, from the spectral perspective, we demonstrate that the weight heterogeneity could lead to the "flattening out" of spectral energy, with a concentration towards the extremes of the spectrum. Building on these insights, we propose the HIerarchical-Perspective Graph Neural Network (HIPGNN) that not only determines spectral energy distribution variations on the spectral perspective, but also perceives detailed protein interaction context on the spatial perspective. Extensive experiments are conducted on two reprocessed datasets STRINGdb and CPDB, and the experimental results demonstrate the superiority of HIPGNN.
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