Explainable discovery of disease biomarkers: The case of ovarian cancer to illustrate the best practice in machine learning and Shapley analysis.

Journal: Journal of biomedical informatics
Published Date: Apr 14, 2023

Abstract

OBJECTIVE: Ovarian cancer is a significant health issue with lasting impacts on the community. Despite recent advances in surgical, chemotherapeutic and radiotherapeutic interventions, they have had only marginal impacts due to an inability to identify biomarkers at an early stage. Biomarker discovery is challenging, yet essential for improving drug discovery and clinical care. Machine learning (ML) techniques are invaluable for recognising complex patterns in biomarkers compared to conventional methods, yet they can lack physical insights into diagnosis. eXplainable Artificial Intelligence (XAI) is capable of providing deeper insights into the decision-making of complex ML algorithms increasing their applicability. We aim to introduce best practice for combining ML and XAI techniques for biomarker validation tasks.

Authors

  • Weitong Huang
    School of Computing, Australian National University, Acton, ACT 2601, Australia. Electronic address: jacob.huang@anu.edu.au.
  • Hanna Suominen
    NICTA, The Australian National University, and University of Canberra, Canberra, Australian Capital Territory, Australia.
  • Tommy Liu
    School of Computing, Australian National University, Acton, ACT 2601, Australia.
  • Gregory Rice
    Inoviq Limited, Notting Hill, Australia; Translational Extracellular Vesicles in Obstetrics and Gynae-Oncology Group, Centre for Clinical Diagnostics, University of Queensland Centre for Clinical Research, Royal Brisbane and Women's Hospital, Faculty of Medicine, The University of Queensland, Brisbane, Australia.
  • Carlos Salomon
    Translational Extracellular Vesicles in Obstetrics and Gynae-Oncology Group, Centre for Clinical Diagnostics, University of Queensland Centre for Clinical Research, Royal Brisbane and Women's Hospital, Faculty of Medicine, The University of Queensland, Brisbane, Australia.
  • Amanda S Barnard
    Data61, CSIRO , 343 Royal Parade, Parkville, Victoria 3052, Australia.

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