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    Machine learning approaches to predict drug efficacy and toxicity in oncology

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    Authors
    Badwan, B. A.
    Liaropoulos, G.
    Kyrodimos, E.
    Skaltsas, D.
    Tsirigos, A.
    Gorgoulis, Vassilis G
    Affiliation
    Intelligencia Inc, New York, NY 10014, USA
    Issue Date
    2023
    
    Metadata
    Show full item record
    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.
    Citation
    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.
    Journal
    Cell Reports Methods
    URI
    http://hdl.handle.net/10541/626170
    DOI
    10.1016/j.crmeth.2023.100413
    PubMed ID
    36936080
    Additional Links
    https://dx.doi.org/10.1016/j.crmeth.2023.100413
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.crmeth.2023.100413
    Scopus Count
    Collections
    All Paterson Institute for Cancer Research

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