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Background <p>The progression of cancer is driven by the accumulation of mutations in driver genes. Many researches promote to identify cancer driver genes. However, most of them ignore the high-order features in the network.</p> Result <p>In this study, we propose a novel method MLGCN-Driver based on multi-layer graph convolutional neural networks (GCN) to boost driver gene identification. MLGCN-Driver employs multi-layer GCN with initial residual connections and identity mappings to learn biological multi-omics features within biological networks. In addition, node2vec algorithm is used to extract the topological structure features of the biological network, and then the features are fed into another multi-layer GCN for feature learning. Meanwhile, the initial residual connections and identity mappings mitigate the over-smooth of features. Finally, the probability of each gene being a driver gene is calculated based on low-dimensional biological features and topological features.</p> Conclusion <p>We applied the MLGCN-Driver on pan-cancer dataset and cancer type-specific datasets. Experimental results demonstrate the excellent performance of MLGCN-Driver in terms of the area under the ROC curve (AUC) and the area under the precision-recall curve (AUPRC) when compared with state-of-the-art approaches.</p>

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MLGCN-Driver: a cancer driver gene identification method based on multi-layer graph convolutional neural network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月30日

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  • Pi-Jing Wei,
  • Jingxin Zhou,
  • Rui-Fen Cao,
  • Yun Ding,
  • Zhenyu Yue,
  • Chun-Hou Zheng

摘要

Background

The progression of cancer is driven by the accumulation of mutations in driver genes. Many researches promote to identify cancer driver genes. However, most of them ignore the high-order features in the network.

Result

In this study, we propose a novel method MLGCN-Driver based on multi-layer graph convolutional neural networks (GCN) to boost driver gene identification. MLGCN-Driver employs multi-layer GCN with initial residual connections and identity mappings to learn biological multi-omics features within biological networks. In addition, node2vec algorithm is used to extract the topological structure features of the biological network, and then the features are fed into another multi-layer GCN for feature learning. Meanwhile, the initial residual connections and identity mappings mitigate the over-smooth of features. Finally, the probability of each gene being a driver gene is calculated based on low-dimensional biological features and topological features.

Conclusion

We applied the MLGCN-Driver on pan-cancer dataset and cancer type-specific datasets. Experimental results demonstrate the excellent performance of MLGCN-Driver in terms of the area under the ROC curve (AUC) and the area under the precision-recall curve (AUPRC) when compared with state-of-the-art approaches.