A Cancer Survival Prediction Method Based on Graph Convolutional Network.

Journal: IEEE transactions on nanobioscience
Published Date:

Abstract

BACKGROUND AND OBJECTIVE: Cancer, as the most challenging part in the human disease history, has always been one of the main threats to human life and health. The high mortality of cancer is largely due to the complexity of cancer and the significant differences in clinical outcomes. Therefore, it will be significant to improve accuracy of cancer survival prediction, which has become one of the main fields of cancer research. Many calculation models for cancer survival prediction have been proposed at present, but most of them generate prediction models only by using single genomic data or clinical data. Multiple genomic data and clinical data have not been integrated yet to take a comprehensive consideration of cancers and predict their survival.

Authors

  • Chunyu Wang
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
  • Junling Guo
  • Ning Zhao
  • Yang Liu
    Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.
  • Xiaoyan Liu
    College of Information Technology, Jilin Agricultural University, Changchun, China.
  • Guojun Liu
  • Maozu Guo
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.