Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data.

Journal: BMC medical genomics
Published Date: Dec 23, 2019

Abstract

BACKGROUND: Understanding the complex biological mechanisms of cancer patient survival using genomic and clinical data is vital, not only to develop new treatments for patients, but also to improve survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause computational challenges to applying conventional survival analysis.

Authors

  • Jie Hao
  • Youngsoon Kim
    Kennesaw State University, Marietta, USA.
  • Tejaswini Mallavarapu
    Analytics and Data Science Institute, Kennesaw State University, Kennesaw, GA, USA.
  • Jung Hun Oh
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Mingon Kang
    Department of Computer Science, Kennesaw State University, Marietta, 30060, Georgia, USA.

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