Biomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients: a hybrid methodology integrating machine learning and explainable artificial intelligence.

Journal: Frontiers in molecular biosciences
Published Date: Dec 18, 2024

Abstract

BACKGROUND: Breast cancer (BC) is a significant cause of morbidity and mortality in women. Although the important role of metabolism in the molecular pathogenesis of BC is known, there is still a need for robust metabolomic biomarkers and predictive models that will enable the detection and prognosis of BC. This study aims to identify targeted metabolomic biomarker candidates based on explainable artificial intelligence (XAI) for the specific detection of BC.

Authors

  • Fatma Hilal Yagin
    Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya, Türkiye.
  • Yasin Gormez
    Department of Management Information Systems, Faculty of Economics and Administrative Sciences, Sivas Cumhuriyet University, Sivas, Türkiye.
  • Fahaid Al-Hashem
    Department of Physiology, College of Medicine, King Khalid University, Abha, Saudi Arabia.
  • Irshad Ahmad
    Department of Medical Rehabilitation Sciences, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia.
  • Fuzail Ahmad
    Respiratory Care Department, College of Applied Sciences, Almareefa University, Riyadh, Saudi Arabia.
  • Luca Paolo Ardigò
    Department of Teacher Education, NLA University College, Oslo, Norway.

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