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dc.contributor.editorCassidy, John W.
dc.contributor.editorTaylor, Belle
dc.date.accessioned2021-02-10T13:35:28Z
dc.date.available2021-02-10T13:35:28Z
dc.date.issued2020
dc.identifierhttps://library.oapen.org/handle/20.500.12657/43405
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/29768
dc.description.abstractThere 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.
dc.languageEnglish
dc.rightsopen access
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence
dc.subject.otherComputers
dc.subject.otherArtificial Intelligence
dc.subject.otherGeneral
dc.titleArtificial Intelligence in Oncology Drug Discovery and Development
dc.typebook
oapen.identifier.doihttps://doi.org/10.5772/intechopen.88376
oapen.relation.isFundedBy969f21b5-ac00-4517-9de2-44973eec6874
oapen.relation.isbn9781789858983
oapen.collectionKnowledge Unlatched (KU)
oapen.imprintIntechOpen
dc.dateSubmitted2020-12-15T13:26:56Z
dc.numbere7b3ced1-1aa0-4c44-9f10-c6bdd14cdc2c
dc.relationisFundedByb818ba9d-2dd9-4fd7-a364-7f305aef7ee9


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Except where otherwise noted, this item's license is described as open access