Towards simplified graph neural networks for identifying cancer driver genes in heterophilic networks.

ABSTRACT: The identification of cancer driver genes is crucial for understanding the complex processes involved in cancer development, progression, and therapeutic strategies. Multi-omics data and biological networks provided by numerous databases enable the application of graph deep learning techniques that incorporate network structures into the deep learning framework. However, most existing methods do not account for the heterophily in the biological networks, which hinders the improvement of model performance. Meanwhile, feature confusion often arises in models based on graph neural networks in such graphs. To address this, we propose a Simplified Graph neural network for identifying Cancer Driver genes in heterophilic networks (SGCD), which comprises primarily two components: a graph convoluti

SUBMITTER: Li X 

PROVIDER: S-EPMC11697181 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature