Prediction and interpretation of cancer survival using graph convolution neural networks.

ABSTRACT: The survival rate of cancer has increased significantly during the past two decades for breast, prostate, testicular, and colon cancer, while the brain and pancreatic cancers have a much lower median survival rate that has not improved much over the last forty years. This has imposed the challenge of finding gene markers for early cancer detection and treatment strategies. Different methods including regression-based Cox-PH, artificial neural networks, and recently deep learning algorithms have been proposed to predict the survival rate for cancers. We established in this work a novel graph convolution neural network (GCNN) approach called Surv_GCNN to predict the survival rate for 13 different cancer types using the TCGA dataset. For each cancer type, 6 Surv_GCNN models with graphs genera

SUBMITTER: Ramirez R 

PROVIDER: S-EPMC8808665 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature