Machine Learning and Deep Learning in Computational Toxicology - Couverture souple

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9783031207327: Machine Learning and Deep Learning in Computational Toxicology

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ISBN 10 : 3031207327 ISBN 13 : 9783031207327

Editeur : Springer International Publishing AG, 2024

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One of the major features is the case studies of applications of machine learning and deep learning in toxicological research that serve as examples for readers to learn how to apply machine learning and deep learning techniques in predictive toxicology.

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� propos de l?auteur

Huixiao Hong is a Senior Biomedical Research and Biomedical Product Assessment Service (SBRBPAS) expert and the chief of Bioinformatics Branch, Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration (FDA), working on the scientific bases for regulatory applications of bioinformatics, cheminformatics, artificial intelligence, and genomics. Before joining the FDA, he was the manager of Bioinformatics Division of Z-Tech, an ICFI company. He held a research scientist position at Sumitomo Chemical Company in Japan and was a visiting scientist at National Cancer Institute at National Institutes of Health. He was also an associate professor and the director of Laboratory of Computational Chemistry at Nanjing University in China. Dr. Hong is a member of steering committee of OpenTox, a member of the board directors of US MidSouth Computational Biology and Bioinformatics Society, and in the leadership circle of US FDA modeling and simulation working group. He published more than 240 scientific papers with a Google Scholar h-index 60. He serves as an associate editor for Experimental Biology and Medicine and an editorial board member for multiple peer-reviewed journals. He received his Ph.D. from Nanjing University in China and conducted research in Leeds University in England.

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: Springer International Publishing AG
Date d'�dition: 2024
Langue: anglais
ISBN 10: 3031207327
ISBN 13: 9783031207327
Reliure: Broch�
Nombre de pages: 654
�diteur: Hong Huixiao
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Machine Learning and Deep Learning in Computational Toxicology (eng)

Hong, Huixiao

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ISBN 10 : 3031207327 ISBN 13 : 9783031207327

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Machine Learning and Deep Learning in Computational Toxicology

Huixiao Hong

Edit� par Springer, Berlin, Springer International Publishing, Springer, 2024

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is a collection of machine learning and deep learning algorithms, methods, architectures, and software tools that have been developed and widely applied in predictive toxicology. It compiles a set of recent applications using state-of-the-art machine learning and deep learning techniques in analysis of a variety of toxicological endpoint data. The contents illustrate those machine learning and deep learning algorithms, methods, and software tools and summarise the applications of machine learning and deep learning in predictive toxicology with informative text, figures, and tables that are contributed by the first tier of experts. One of the major features is the case studies of applications of machine learning and deep learning in toxicological research that serve as examples for readers to learn how to apply machine learning and deep learning techniques in predictive toxicology. This book is expected to provide a reference for practical applications of machine learning and deep learning in toxicological research. It is a useful guide for toxicologists, chemists, drug discovery and development researchers, regulatory scientists, government reviewers, and graduate students. The main benefit for the readers is understanding the widely used machine learning and deep learning techniques and gaining practical procedures for applying machine learning and deep learning in predictive toxicology. 635 pp. Englisch. N� de r�f. du vendeur 9783031207327

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Machine Learning and Deep Learning in Computational Toxicology

Edit� par Springer, Berlin|Springer International Publishing|Springer, 2024

ISBN 10 : 3031207327 ISBN 13 : 9783031207327

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book is a collection of machine learning and deep learning algorithms, methods, architectures, and software tools that have been developed and widely applied in predictive toxicology. It compiles a set of recent applications using state-of-the-art mach. N� de r�f. du vendeur 1351394202

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Machine Learning and Deep Learning in Computational Toxicology

Huixiao Hong

Edit� par Springer, Springer Feb 2024, 2024

ISBN 10 : 3031207327 ISBN 13 : 9783031207327

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is a collection of machine learning and deep learning algorithms, methods, architectures, and software tools that have been developed and widely applied in predictive toxicology. It compiles a set of recent applications using state-of-the-art machine learning and deep learning techniques in analysis of a variety of toxicological endpoint data. The contents illustrate those machine learning and deep learning algorithms, methods, and software tools and summarise the applications of machine learning and deep learning in predictive toxicology with informative text, figures, and tables that are contributed by the first tier of experts. One of the major features is the case studies of applications of machine learning and deep learning in toxicological research that serve as examples for readers to learn how to apply machine learning and deep learning techniques in predictive toxicology. This book is expected to provide a reference for practical applications of machine learning anddeep learning in toxicological research. It is a useful guide for toxicologists, chemists, drug discovery and development researchers, regulatory scientists, government reviewers, and graduate students. The main benefit for the readers is understanding the widely used machine learning and deep learning techniques and gaining practical procedures for applying machine learning and deep learning in predictive toxicology.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 676 pp. Englisch. N� de r�f. du vendeur 9783031207327

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Machine Learning and Deep Learning in Computational Toxicology

Huixiao Hong

Edit� par Springer International Publishing, 2024

ISBN 10 : 3031207327 ISBN 13 : 9783031207327

Neuf Taschenbuch

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is a collection of machine learning and deep learning algorithms, methods, architectures, and software tools that have been developed and widely applied in predictive toxicology. It compiles a set of recent applications using state-of-the-art machine learning and deep learning techniques in analysis of a variety of toxicological endpoint data. The contents illustrate those machine learning and deep learning algorithms, methods, and software tools and summarise the applications of machine learning and deep learning in predictive toxicology with informative text, figures, and tables that are contributed by the first tier of experts. One of the major features is the case studies of applications of machine learning and deep learning in toxicological research that serve as examples for readers to learn how to apply machine learning and deep learning techniques in predictive toxicology. This book is expected to provide a reference for practical applications of machine learning anddeep learning in toxicological research. It is a useful guide for toxicologists, chemists, drug discovery and development researchers, regulatory scientists, government reviewers, and graduate students. The main benefit for the readers is understanding the widely used machine learning and deep learning techniques and gaining practical procedures for applying machine learning and deep learning in predictive toxicology. N� de r�f. du vendeur 9783031207327

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