A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.

Journal: BMC bioinformatics
Published Date: May 15, 2023

Abstract

BACKGROUND: There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, which constrain their deployment in biomedical settings.

Authors

  • Magdalena Wysocka
    Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, CRUK Manchester Institute, University of Manchester, Oxford Rd, Manchester, M13 9 PL, UK. magdalena.wysocka@manchester.ac.uk.
  • Oskar Wysocki
    Cancer Research UK Manchester Institute, University of Manchester, Oxford Rd, Manchester M13 9PL, United Kingdom; Idiap Research Institute, National University of Sciences, Rue Marconi 19, CH - 1920 Martigny, Switzerland.
  • Marie Zufferey
    Idiap Research Institute, National University of Sciences, Rue Marconi 19, CH - 1920, Martigny, Switzerland.
  • Dónal Landers
    DeLondra Oncology Ltd, 38, Carlton Avenue, Wilmslow SK9 4EP, United Kingdom.
  • Andre Freitas
    Department of Computer Science, University of Manchester, Manchester, UK.

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