Publikation
Rethinking Cancer Gene Identification through Graph Anomaly Analysis
Yilong Zang; Lingfei Ren; Yue Li; Zhikang Wang; David Antony Selby; Zheng Wang; Sebastian Vollmer; Hongzhi Yin; Jiangning Song; Junhang Wu
In: The 39th Annual AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence (AAAI-2025), 39th, February 25 - March 4, Philadelphia, PA, USA, Association for the Advancement of Artificial Intelligence, 2025.
Zusammenfassung
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.