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01911nma a2200325 u 4500 |
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EBX01000000000000001148674 |
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00000000000000.0 |
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210512 ||| eng |
| 020 |
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|a 9781789858983
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| 020 |
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|a doi.org/10.5772/intechopen.88376
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| 100 |
1 |
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|a Cassidy, John W.
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| 245 |
0 |
0 |
|a Artificial Intelligence in Oncology Drug Discovery and Development
|h Elektronische Ressource
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| 260 |
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|c 2020
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| 653 |
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|a Artificial intelligence / bicssc
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a General
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| 653 |
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|a Computers
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| 700 |
1 |
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|a Taylor, Belle
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| 700 |
1 |
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|a Cassidy, John W.
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| 700 |
1 |
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|a Taylor, Belle
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-nc/4.0/legalcode
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| 024 |
8 |
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|a https://doi.org/10.5772/intechopen.88376
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| 856 |
4 |
0 |
|u https://library.oapen.org/bitstream/20.500.12657/43405/1/external_content.pdf
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/29768
|z DOAB: description of the publication
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| 082 |
0 |
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|a 700
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| 520 |
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|a 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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