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Machine Learning and Deep Learning in Computational Toxicology (Record no. 103259)

MARC details
000 -LEADER
fixed length control field 04417nam a22004695i 4500
001 - CONTROL NUMBER
control field 978-3-031-20730-3
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240729135807.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 230207s2023 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031207303
-- 978-3-031-20730-3
072 #7 - SUBJECT CATEGORY CODE
Subject category code MMGT
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code MED106000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code MKGT
Source thema
245 10 - TITLE STATEMENT
Title Machine Learning and Deep Learning in Computational Toxicology
250 ## - EDITION
Edition statement 1st ed. 2023.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Cham :
Name of producer, publisher, distributor, manufacturer Springer International Publishing :
-- Imprint: Springer,
Date of production, publication, distribution, manufacture, or copyright notice 2023.
300 ## - PHYSICAL DESCRIPTION
Physical description XIX, 635 p. 149 illus., 124 illus. in color.
Other physical details online resource.
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
490 1# - SERIES TITLE
Series statement Computational Methods in Engineering & the Sciences,
International Standard Serial Number 2662-4877
505 0# - CONTENTS
Contents 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 ## - ABSTRACT
Abstract 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. .
650 #0 - SUBJECT HEADINGS
Subject term Toxicology.
9 (RLIN) 8159
650 #0 - SUBJECT HEADINGS
Subject term Machine learning.
650 #0 - SUBJECT HEADINGS
Subject term Artificial intelligence.
650 14 - SUBJECT HEADINGS
Subject term Toxicology.
9 (RLIN) 8159
650 24 - SUBJECT HEADINGS
Subject term Machine Learning.
650 24 - SUBJECT HEADINGS
Subject term Artificial Intelligence.
700 1# - ADDED PERSONAL NAME
Added personal author Hong, Huixiao.
Relator term editor.
710 2# - ADDED CORPORATE NAME
Added corporate author SpringerLink (Online service)
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Computational Methods in Engineering & the Sciences,
International Standard Serial Number 2662-4877
856 ## - ONLINE RESOURCE
Uniform Resource Identifier <a href="#gotoholdings">#gotoholdings</a>
Link text Access resource
245 ## - TITLE STATEMENT
Medium [E-Book]
347 ## - DIGITAL FILE CHARACTERISTICS
File type text file
Encoding format PDF
Source rda
912 ## -
-- ZDB-2-SBL
912 ## -
-- ZDB-2-SXB
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Shelving location Date acquired Source of acquisition Total Checkouts Date last seen Uniform Resource Identifier Price effective from Koha item type
        Hillingdon Hospitals Library Services (Hillingdon Hospitals NHS Foundation) Hillingdon Hospitals Library Services (Hillingdon Hospitals NHS Foundation) Online 02/05/2024 Springer BiomedLifeSc_2023   02/05/2024 https://go.openathens.net/redirector/nhs?url=https://doi.org/10.1007/978-3-031-20730-3 02/05/2024 Electronic book
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