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008			210129p20202020xx o u00| u eng d
037			_5BiblioBoard
245	0	0	_aArtificial Intelligence in Oncology Drug Discovery and Development _cJohn W. Cassidy, Belle Taylor.
020			_a9781789858983
024	8		_ahttps://doi.org/10.5772/intechopen.88376
029	1		_ahttps://library.biblioboard.com/ext/api/media/e7b3ced1-1aa0-4c44-9f10-c6bdd14cdc2c/assets/thumbnail.jpg
040			_aScCtBLL _cScCtBLL
506	0		_aAccess copy available to the general public. _fUnrestricted _2star
700	1		_aCassidy, John W. _eeditor.
700	1		_aTaylor, Belle _eeditor.
264		1	_bIntechOpen,
300			_a1 online resource (1 p.)
520			_aThere 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.
588	0		_aDescription based on print version record.
590			_aIntechOpen Engineering 2019 - 2021
650		7	_aComputers / Artificial Intelligence _2bisacsh
650		0	_aComputers
655		0	_aElectronic books.
758			_iIs found in: _aKnowledge Unlatched _1https://openresearchlibrary.org/module/2774bc74-146a-484f-a7ba-ab1d6a09bbfb
856	4	0	_uhttps://openresearchlibrary.org/content/e7b3ced1-1aa0-4c44-9f10-c6bdd14cdc2c _zView this content on Open Research Library. _70
999			_c32534 _d32534