Rethinking Cancer Gene Identification through Graph Anomaly Analysis
This work addresses the challenge of more accurately identifying cancer genes for biomedical research, representing an incremental improvement by introducing a novel graph anomaly analysis approach.
The study tackled the problem of identifying cancer genes by analyzing graph anomalies in protein-protein interaction networks, finding that cancer genes exhibit weight heterogeneity and spectral energy flattening, and proposed HIPGNN, which achieved superior performance on datasets like STRINGdb and CPDB.
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.