Improving Cancer Gene Identification via Mixture-of-Experts-Based Graph Representation Learning

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Accurately identifying cancer driver genes is crucial for understanding tumorigenesis and advancing precision oncology. However, integrating multi-omics data within complex biological networks remains challenging, particularly when it comes to capturing diverse structural information and leveraging the distinct signals from different omics modalities. While graphbased methods have demonstrated high accuracy in cancer gene identification, they might overlook the heterogeneity between omics features. To address this limitation, we propose CGI-MoE, a Mixture-of-Experts-inspired graph representation learning framework that incorporates omics-feature-specific expert modules based on graph transformers, together with adaptive gating and subgraph aggregation mechanisms. CGI-MoE extracts both local and global structural encodings for each node by sampling multiple subgraphs, enabling the model to capture comprehensive and robust network features. Each expert module focuses on a specific omics modality, and their outputs are fused by a lightweight gating network that dynamically weighs their contributions. When evaluated on both homogeneous and heterogeneous benchmark datasets, CGI-MoE achieves state-of-the-art performance in terms of accuracy, AUC, and AUPR, consistently surpassing existing methods. Ablation studies further highlight the critical roles of each component in achieving robust performance. Using the trained models, CGI-MoE predicted 46 novel cancer gene candidates from all unlabeled genes, demonstrating its potential for novel discovery and for deepening our understanding of cancer development. The code is available at https://github.com/moomight/CGI-MoE.

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