Explainable Multilayer Graph Neural Network for Cancer Gene Prediction

Understand

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 explanation 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.