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Machine learning and deep learning in computational toxicology

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning and deep learning in computational toxicologyedited by Huixiao Hong.
other author:	Hong, Huixiao.
Published:	Cham :Springer International Publishing :2023.
Description:	xix, 635 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	ToxicologyData processing.
Online resource:	https://doi.org/10.1007/978-3-031-20730-3
ISBN:	9783031207303$q(electronic bk.)

Machine learning and deep learning in computational toxicology
Machine learning and deep learning in computational toxicology[electronic resource] /edited by Huixiao Hong. - Cham :Springer International Publishing :2023. - xix, 635 p. :ill., digital ;24 cm. - Computational methods in engineering & the sciences,2662-4877. - Computational methods in engineering & the sciences..

Machine Learning and Deep Learning Promotes Predictive Toxicology for Risk Assessment of Chemicals -- Multi-Modal Deep Learning Approaches for Molecular Toxicity prediction -- Emerging Machine Learning Techniques in Predicting Adverse Drug Reactions -- Drug Effect Deep Learner Based on Graphical Convolutional Network -- AOP Based Machine Learning for Toxicity Prediction -- Graph Kernel Learning for Predictive Toxicity Models -- Optimize and Strengthen Machine Learning Models Based on in vitro Assays with Mecha-nistic Knowledge and Real-World Data -- Multitask Learning for Quantitative Structure-Activity Relationships: A Tutorial -- Isalos Predictive Analytics Platform: Cheminformatics, Nanoinformatics and Data Mining Applications -- ED Profiler: Machine Learning Tool for Screening Potential Endocrine Disrupting Chemicals -- Quantitative Target-specific Toxicity Prediction Modeling (QTTPM): Coupling Machine Learning with Dynamic Protein-Ligand Interaction Descriptors (dyPLIDs) to Predict Androgen Receptor-mediated Toxicity -- Mold2 Descriptors Facilitate Development of Machine Learning and Deep Learning Models for Predicting Toxicity of Chemicals -- Applicability Domain Characterization for Machine Learning QSAR Models -- Controlling for Confounding in Complex Survey Machine Learning Models to Assess Drug Safety and Risk.

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: 9783031207303$q(electronic bk.)

Standard No.: 10.1007/978-3-031-20730-3doiSubjects--Topical Terms:

667245
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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505 0 $a Machine Learning and Deep Learning Promotes Predictive Toxicology for Risk Assessment of Chemicals -- Multi-Modal Deep Learning Approaches for Molecular Toxicity prediction -- Emerging Machine Learning Techniques in Predicting Adverse Drug Reactions -- Drug Effect Deep Learner Based on Graphical Convolutional Network -- AOP Based Machine Learning for Toxicity Prediction -- Graph Kernel Learning for Predictive Toxicity Models -- Optimize and Strengthen Machine Learning Models Based on in vitro Assays with Mecha-nistic Knowledge and Real-World Data -- Multitask Learning for Quantitative Structure-Activity Relationships: A Tutorial -- Isalos Predictive Analytics Platform: Cheminformatics, Nanoinformatics and Data Mining Applications -- ED Profiler: Machine Learning Tool for Screening Potential Endocrine Disrupting Chemicals -- Quantitative Target-specific Toxicity Prediction Modeling (QTTPM): Coupling Machine Learning with Dynamic Protein-Ligand Interaction Descriptors (dyPLIDs) to Predict Androgen Receptor-mediated Toxicity -- Mold2 Descriptors Facilitate Development of Machine Learning and Deep Learning Models for Predicting Toxicity of Chemicals -- Applicability Domain Characterization for Machine Learning QSAR Models -- Controlling for Confounding in Complex Survey Machine Learning Models to Assess Drug Safety and Risk.
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 667245
650 0 $a Machine learning. $3 188639
650 1 4 $a Toxicology. $3 206218
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Artificial Intelligence. $3 212515
700 1 $a Hong, Huixiao. $3 841379
710 2 $a SpringerLink (Online service) $3 273601
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830 0 $a Computational methods in engineering & the sciences. $3 869117
856 4 0 $u https://doi.org/10.1007/978-3-031-20730-3
950 $a Biomedical and Life Sciences (SpringerNature-11642)