相互作用体
计算生物学
计算机科学
鉴定(生物学)
癌变
生物
数据集成
生物网络
癌症
基因
遗传学
基因组学
生物信息学
基因组
数据挖掘
植物
作者
Roman Schulte-Sasse,Stefan Budach,Denes Hnisz,Annalisa Marsico
标识
DOI:10.1038/s42256-021-00325-y
摘要
The increase in available high-throughput molecular data creates computational challenges for the identification of cancer genes. Genetic as well as non-genetic causes contribute to tumorigenesis, and this necessitates the development of predictive models to effectively integrate different data modalities while being interpretable. We introduce EMOGI, an explainable machine learning method based on graph convolutional networks to predict cancer genes by combining multiomics pan-cancer data—such as mutations, copy number changes, DNA methylation and gene expression—together with protein–protein interaction (PPI) networks. EMOGI was on average more accurate than other methods across different PPI networks and datasets. We used layer-wise relevance propagation to stratify genes according to whether their classification was driven by the interactome or any of the omics levels, and to identify important modules in the PPI network. We propose 165 novel cancer genes that do not necessarily harbour recurrent alterations but interact with known cancer genes, and we show that they correspond to essential genes from loss-of-function screens. We believe that our method can open new avenues in precision oncology and be applied to predict biomarkers for other complex diseases. Identifying cancer driver genes from high-throughput genomic data is an important task to understand the molecular basis of cancer and to develop new treatments including precision medicine. To tackle this challenge, EMOGI, a new deep learning method based on graph convolutional networks is developed, which combines protein–protein interaction networks with multiomics datasets.
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