Mitigating Oversmoothing in Hypergraph Neural Networks for Enhanced Cancer-Driver Gene Prediction

Predicting cancer-driver genes is a significant task in cancer research, and hypergraph neural networks are effective tools for capturing complex relationships among genes. However, these methods typically experience performance degradation due to the oversmoothing problem, which indicates the situation where node distinctiveness decreases as network depth increases. In this study, we evaluated several approaches to mitigate the oversmoothing problem in cancer-driver gene prediction using hypergraph neural networks. Specifically, the deep learning model incorporating oversmoothing mitigation techniques achieved a 3.22% improvement in terms of the area under the receiver operating characteristic curve, reaching the score 0.931. Moreover, in terms of the area under the precision-recall curve, the model showed a score of 0.881, reflecting a 14.42% improvement. The experimental results demonstrate that, even as the network depth increases, the selected approaches effectively handle oversmoothing, leading to improved performance to predict cancer-driver genes.