Explainable Multilayer Graph Neural Network for cancer gene prediction.

ABSTRACT:

Motivation

The identification of cancer genes is a critical yet challenging problem in cancer genomics research. Existing computational methods, including deep graph neural networks, fail to exploit the multilayered gene-gene interactions or provide limited explanations for their predictions. These methods are restricted to a single biological network, which cannot capture the full complexity of tumorigenesis. Models trained on different biological networks often yield different and even opposite cancer gene predictions, hindering their trustworthy adaptation. Here, we introduce an Explainable Multilayer Graph Neural Network (EMGNN) approach to identify cancer genes by leveraging multiple gene-gene interaction networks and pan-cancer multi-omics data. Unlike conventional graph lear

SUBMITTER: Chatzianastasis M 

PROVIDER: S-EPMC10636280 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature