|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910985677503321 |
|
|
Autore |
Srivastava Ruby |
|
|
Titolo |
Artificial Intelligence Multiomics in Precision Oncology |
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Newcastle-upon-Tyne : , : Cambridge Scholars Publishing, , 2023 |
|
©2023 |
|
|
|
|
|
|
|
|
|
ISBN |
|
9781527500891 |
9781443895200 |
|
|
|
|
|
|
|
|
Edizione |
[1st ed.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (517 pages) |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Artificial intelligence |
Precision medicine |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Nota di contenuto |
|
Intro -- Dedication -- Table of Contents -- Preface -- Acknowledgements -- Chapter 1 -- Chapter 2 -- Chapter 3 -- Chapter 4 -- Chapter 5 -- Chapter 6 -- Chapter 7 -- Chapter 8 -- Chapter 9 -- Chapter 10 -- Chapter 11 -- Chapter 12 -- Chapter 13. |
|
|
|
|
|
|
|
|
Sommario/riassunto |
|
Advances in next-generation technology (NGS) coupled with a deep understanding of cancer biology have promoted the rational design of target therapy towards precision oncology. Artificial Intelligence (AI)-integrated machine learning techniques are also increasingly used today to tackle the challenges of scalability and high dimensionality data and to transform multiomics data into clinically actionable knowledge. AI tools are used to support clinical decision making and improve clinical efficiency, while delivering safe and high value care. This book provides comprehensive analysis of such techniques and advancements of AI-based clinical cancer research in the improvement of cancer prognosis and diagnosis, resulting in enhanced prediction rates and survival of cancer patients. |
|
|
|
|
|
|
|