Role of machine learning in molecular pathology for breast cancer: A review on gene expression profiling and RNA sequencing application.

Journal: Critical reviews in oncology/hematology
Published Date: May 24, 2025

Abstract

INTRODUCTION: Breast cancer is the most prevalent cancer among women, with growing incidence and mortality rates. Regardless of remarkable progress in cancer research, breast cancer remains a major concern due to its complex nature. These factors underscore the necessity of innovative research and diagnostic tools. Attention to gene signatures and biotechnology methods have shown significant performance in the diagnosis and management of breast cancer. Currently, artificial intelligence (AI) is known as a revolutionary tool to analyze data, identify biomarkers, and enrich diagnostic and prognostic accuracy. Therefore, the integration of breast cancer datasets with artificial intelligence can play a crucial role in the control of breast cancer. This review explores advanced machine learning techniques to analyze transcriptomic data while focusing on breast cancer subtype classification and its potential impact and limitations.

Authors

  • Sahar Rezaei
    Department of Nuclear Medicine, Medical School, Tabriz University of Medical Sciences, Tabriz, Iran.
  • Zeinab Hamedani
    bInternational School of Medicine, Zhejiang University, Zhejiang, China.
  • Kousar Ahmadi
    Department of Anatomy, Faculty of Medicine, Urmia University of Medical Sciences, Urmia, Iran.
  • Parna Ghannadikhosh
    Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran.
  • Alireza Motamedi
    Student Research Committee, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
  • Maedeh Athari
    Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran.
  • Hengameh Yousefi
    Student Research Committee, School of Medicine, Islamic Azad University, Kerman Branch, Kerman, Iran.
  • Amir Hossein Rajabi
    Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Alireza Abbasi
    School of Engineering and Information Technology, University of New South Wales, Canberra, NSW, 2006, Australia.
  • Hossein Arabi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.

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