Recent progress in machine learning approaches for predicting carcinogenicity in drug development.

Journal: Expert opinion on drug metabolism & toxicology
PMID: 38742542

Abstract

INTRODUCTION: This review explores the transformative impact of machine learning (ML) on carcinogenicity prediction within drug development. It discusses the historical context and recent advancements, emphasizing the significance of ML methodologies in overcoming challenges related to data interpretation, ethical considerations, and regulatory acceptance.

Authors

  • Nguyen Quoc Khanh Le
    In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan; AIBioMed Research Group, Taipei Medical University, Taipei 110, Taiwan; Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan. Electronic address: khanhlee@tmu.edu.tw.
  • Thi-Xuan Tran
    University of Economics and Business Administration, Thai Nguyen University, Thai Nguyen, Vietnam.
  • Phung-Anh Nguyen
    Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan.
  • Trang-Thi Ho
    Research Center for Information Technology Innovation, Academia Sinica, Taipei 10607, Taiwan.
  • Van-Nui Nguyen
    University of Information and Communication Technology, Thai Nguyen University, Thai Nguyen, Viet Nam.

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