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        Artificial Intelligence in Oncology Drug Discovery and Development

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        Contributor(s)
        Cassidy, John W. (editor)
        Taylor, Belle (editor)
        Collection
        Knowledge Unlatched (KU)
        Number
        e7b3ced1-1aa0-4c44-9f10-c6bdd14cdc2c
        Language
        English
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        Abstract
        There exists a profound conflict at the heart of oncology drug development. The efficiency of the drug development process is falling, leading to higher costs per approved drug, at the same time personalised medicine is limiting the target market of each new medicine. Even as the global economic burden of cancer increases, the current paradigm in drug development is unsustainable. In this book, we discuss the development of techniques in machine learning for improving the efficiency of oncology drug development and delivering cost-effective precision treatment. We consider how to structure data for drug repurposing and target identification, how to improve clinical trials and how patients may view artificial intelligence.
        URI
        https://library.oapen.org/handle/20.500.12657/43405
        Keywords
        Computers; Artificial Intelligence; General
        DOI
        https://doi.org/10.5772/intechopen.88376
        ISBN
        9781789858983
        Publisher
        InTechOpen
        Publisher website
        https://www.intechopen.com/
        Publication date and place
        2020
        Grantor
        • Knowledge Unlatched
        Imprint
        IntechOpen
        Classification
        Artificial intelligence
        Rights
        https://creativecommons.org/licenses/by-nc/4.0/legalcode
        • Harvested from KU

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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