Identifying cancer driver genes based on multi-view heterogeneous graph convolutional network and self-attention mechanism.

ABSTRACT:

Background

Correctly identifying the driver genes that promote cell growth can significantly assist drug design, cancer diagnosis and treatment. The recent large-scale cancer genomics projects have revealed multi-omics data from thousands of cancer patients, which requires to design effective models to unlock the hidden knowledge within the valuable data and discover cancer drivers contributing to tumorigenesis.

Results

In this work, we propose a graph convolution network-based method called MRNGCN that integrates multiple gene relationship networks to identify cancer driver genes. First, we constructed three gene relationship networks, including the gene-gene, gene-outlying gene and gene-miRNA networks. Then, genes learnt feature presentations from the three networks throug

SUBMITTER: Peng W 

PROVIDER: S-EPMC9838012 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature