Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data.

Journal: Bioinformatics (Oxford, England)
Published Date: Mar 1, 2020

Abstract

MOTIVATION: Cancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes.

Authors

  • Runpu Chen
    Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14214, USA.
  • Le Yang
    Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14214, USA.
  • Steve Goodison
    Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL 32224, USA.
  • Yijun Sun
    Genetics, Genomics, and Bioinformatics Graduate Program, University at Buffalo, Buffalo, NY, USA.

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