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

ABSTRACT:

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 a

SUBMITTER: Wei PJ 

PROVIDER: S-EPMC12482577 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature