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Machine learning approaches to predict drug efficacy and toxicity in oncology
Badwan, B. A. Liaropoulos, G. Kyrodimos, E. Skaltsas, D. Tsirigos, A. Gorgoulis, Vassilis G
Badwan, B. A.
Liaropoulos, G.
Kyrodimos, E.
Skaltsas, D.
Tsirigos, A.
Gorgoulis, Vassilis G
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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.
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2023
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Badwan BA, Liaropoulos G, Kyrodimos E, Skaltsas D, Tsirigos A, Gorgoulis VG. Machine learning approaches to predict drug efficacy and toxicity in oncology. Cell reports methods. 2023 Feb 27;3(2):100413. PubMed PMID: 36936080. Pubmed Central PMCID: PMC10014302. Epub 2023/03/21. eng.