Machine learning approaches to predict drug efficacy and toxicity in oncology.

Journal: Cell reports methods
Published Date: Feb 21, 2023

Abstract

In recent years, there has been a surge of interest in using machine learning algorithms (MLAs) in oncology, particularly for biomedical applications such as drug discovery, drug repurposing, diagnostics, clinical trial design, and pharmaceutical production. MLAs have the potential to provide valuable insights and predictions in these areas by representing both the disease state and the therapeutic agents used to treat it. To fully utilize the capabilities of MLAs in oncology, it is important to understand the fundamental concepts underlying these algorithms and how they can be applied to assess the efficacy and toxicity of therapeutics. In this perspective, we lay out approaches to represent both the disease state and the therapeutic agents used by MLAs to derive novel insights and make relevant predictions.

Authors

  • Bara A Badwan
    School of Engineering and Applied Science, Yale University, New Haven, CT 06511, USA.
  • Gerry Liaropoulos
    Intelligencia Inc, New York, NY 10014, USA.
  • Efthymios Kyrodimos
    First ENT Department, Hippocration Hospital, National Kapodistrian University of Athens, Athens, GR 11527, Greece.
  • Dimitrios Skaltsas
    Intelligencia Inc., New York, New York.
  • Aristotelis Tsirigos
    Department of Pathology, NYU School of Medicine, New York, NY 10016, USA; Laura and Isaac Perlmutter Cancer Center, NYU School of Medicine, New York, NY 10016, USA; Applied Bioinformatics Laboratories, NYU School of Medicine, New York, NY 10016, USA. Electronic address: aristotelis.tsirigos@nyulangone.org.
  • Vassilis G Gorgoulis
    Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Str., Athens GR-11527, Greece; Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias Str, Athens GR-11527, Greece; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, Manchester Cancer Research Centre, NIHR Manchester Biomedical Research Centre, University of Manchester, Manchester M20 4GJ, UK; Center for New Biotechnologies and Precision Medicine, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias Str, Athens GR-11527, Greece. Electronic address: vgorg@med.uoa.gr.

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