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Machine learning and deep learning i...

Hong, Huixiao.

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Machine learning and deep learning in computational toxicology /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine learning and deep learning in computational toxicology // Huixiao Hong, editor.
其他作者:	Hong, Huixiao.
出版者:	Cham, Switzerland :Springer, : 2023.,
面頁冊數:	xix, 654 p. :ill. (some col.) ;25 cm.
標題:	Toxicology - Data processing. -
ISBN:	9783031207297

Machine learning and deep learning in computational toxicology /
Machine learning and deep learning in computational toxicology /Huixiao Hong, editor. - Cham, Switzerland :Springer,2023. - xix, 654 p. :ill. (some col.) ;25 cm. - Computational Methods in Engineering & the Sciences. - Computational methods in engineering & the sciences..

Includes bibliographical references.

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.

ISBN: 9783031207297EUR149.99Subjects--Topical Terms:

2045587
Toxicology
--Data processing.

LC Class. No.: RA1193.4 / .M33 2023

Dewey Class. No.: 615.900285631

Machine learning and deep learning in computational toxicology /
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020 $a 9783031207297 $q (hbk.) : $c EUR149.99
020 $a 3031207297 $q (hbk.)
020 $a 3031207300 $q (ebk.)
020 $a 9783031207303 $q (ebk.)
035 $a (OCoLC)1369597247 $z (OCoLC)1369600508 $z (OCoLC)1369663801
040 $a YDX $b eng $e aacr2 $c YDX $d GW5XE $d EBLCP $d UKAHL $d YDX
050 # 4 $a RA1193.4 $b .M33 2023
082 0 4 $a 615.900285631 $2 23/eng/20230215
245 0 0 $a Machine learning and deep learning in computational toxicology / $c Huixiao Hong, editor.
260 # $a Cham, Switzerland : $b Springer, $c 2023.
300 $a xix, 654 p. : $b ill. (some col.) ; $c 25 cm.
490 1 $a Computational Methods in Engineering & the Sciences
504 $a Includes bibliographical references.
520 # $a 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.
650 # 0 $a Toxicology $x Data processing. $3 2045587
650 # 0 $a Machine learning. $3 533906
700 1 # $a Hong, Huixiao. $3 3410275
830 0 $a Computational methods in engineering & the sciences. $3 3524579