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oapen-20.500.12657-434052021-01-25T13:50:36Z Artificial Intelligence in Oncology Drug Discovery and Development Cassidy, John W. Taylor, Belle Computers Artificial Intelligence General bic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence 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. 2020-12-15T13:26:56Z 2020-12-15T13:26:56Z 2020 book 9781789858983 https://library.oapen.org/handle/20.500.12657/43405 eng application/pdf n/a external_content.pdf InTechOpen IntechOpen https://doi.org/10.5772/intechopen.88376 e7b3ced1-1aa0-4c44-9f10-c6bdd14cdc2c https://doi.org/10.5772/intechopen.88376 09f6769d-48ed-467d-b150-4cf2680656a1 b818ba9d-2dd9-4fd7-a364-7f305aef7ee9 9781789858983 Knowledge Unlatched (KU) IntechOpen Knowledge Unlatched open access
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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.
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