LEADER 04403nam 2200481 450 001 9910659493003321 005 20230511162703.0 010 $a9783031207303$b(electronic bk.) 010 $z9783031207297 024 7 $a10.1007/978-3-031-20730-3 035 $a(MiAaPQ)EBC7192839 035 $a(Au-PeEL)EBL7192839 035 $a(CKB)26105539200041 035 $a(DE-He213)978-3-031-20730-3 035 $a(PPN)268207887 035 $a(EXLCZ)9926105539200041 100 $a20230511d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine learning and deep learning in computational toxicology /$fHuixiao Hong 205 $a1st ed. 2023. 210 1$aCham, Switzerland :$cSpringer,$d[2023] 210 4$d©2023 215 $a1 online resource (654 pages) 225 1 $aComputational Methods in Engineering & the Sciences,$x2662-4877 311 08$aPrint version: Hong, Huixiao Machine Learning and Deep Learning in Computational Toxicology Cham : Springer International Publishing AG,c2023 9783031207297 320 $aIncludes bibliographical references. 327 $aMachine 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. . 330 $aThis book is a collection of machine learning and deep learning algorithms, methods, architectures, and software tools that have been developed and widely applied in predictive toxicology. It compiles a set of recent applications using state-of-the-art machine learning and deep learning techniques in analysis of a variety of toxicological endpoint data. The contents illustrate those machine learning and deep learning algorithms, methods, and software tools and summarise the applications of machine learning and deep learning in predictive toxicology with informative text, figures, and tables that are contributed by the first tier of experts. One of the major features is the case studies of applications of machine learning and deep learning in toxicological research that serve as examples for readers to learn how to apply machine learning and deep learning techniques in predictive toxicology. This book is expected to provide a reference for practical applications of machine learning and deep learning in toxicological research. It is a useful guide for toxicologists, chemists, drug discovery and development researchers, regulatory scientists, government reviewers, and graduate students. The main benefit for the readers is understanding the widely used machine learning and deep learning techniques and gaining practical procedures for applying machine learning and deep learning in predictive toxicology. . 410 0$aComputational Methods in Engineering & the Sciences,$x2662-4877 606 $aMachine learning 615 0$aMachine learning. 676 $a016.34951249 700 $aHong$b Huixiao$01312227 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910659493003321 996 $aMachine Learning and Deep Learning in Computational Toxicology$93030798 997 $aUNINA LEADER 00750nam0-2200241 --450 001 9910732899003321 005 20240213092126.0 100 $a20221104d1964----kmuy0itay5050 ba 101 1 $afre$ager 102 $aFR 105 $a 001yy 200 1 $aNietzsche$econsiderations inactuelles$e(unzeit gemasse betrachtungen)$gtraduction et preface par Geneviev Bianquis 210 $aParis$cAubier éditions Mountaigne$d1964 215 $a394 p.$d18 cm 225 1 $aCollection bilingue 700 1$aVercellone,$bFederico$f<1955- >$0157432 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910732899003321 952 $aDFT A92.13 NIEF/S 19$b2023/2637$fFLFBC 959 $aFLFBC 996 $aNietzsche$93394280 997 $aUNINA