LEADER 04786nam 22006015 450 001 9910659493003321 005 20250627125307.0 010 $a9783031207303$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 $a20230207d2023 u| 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 /$fedited by Huixiao Hong 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 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 $aThis book is a collection of machine learning and deep learning algorithms, methods, architectures, and software tools that have been developed and widely applied in predictive toxicology. It compiles a set of recent applications using state-of-the-art machine learning and deep learning techniques in analysis of a variety of toxicological endpoint data. The contents illustrate those machine learning and deep learning algorithms, methods, and software tools and summarise the applications of machine learning and deep learning in predictive toxicology with informative text, figures, and tables that are contributed by the first tier of experts. One of the major features is the case studies of applications of machine learning and deep learning in toxicological research that serve as examples for readers to learn how to apply machine learning and deep learning techniques in predictive toxicology. This book is expected to provide a reference for practical applications of machine learning anddeep learning in toxicological research. It is a useful guide for toxicologists, chemists, drug discovery and development researchers, regulatory scientists, government reviewers, and graduate students. The main benefit for the readers is understanding the widely used machine learning and deep learning techniques and gaining practical procedures for applying machine learning and deep learning in predictive toxicology. . 410 0$aComputational Methods in Engineering & the Sciences,$x2662-4877 606 $aToxicology 606 $aMachine learning 606 $aArtificial intelligence 606 $aToxicology 606 $aMachine Learning 606 $aArtificial Intelligence 615 0$aToxicology. 615 0$aMachine learning. 615 0$aArtificial intelligence. 615 14$aToxicology. 615 24$aMachine Learning. 615 24$aArtificial Intelligence. 676 $a016.34951249 676 $a615.900285631 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 01969nam0 22004333i 450 001 CFI0174204 005 20251003044129.0 010 $a8821705293 100 $a19920509d1990 ||||0itac50 ba 101 | $aita 102 $ait 181 1$6z01$ai $bxxxe 182 1$6z01$an 200 1 $aˆIl ‰diritto del commercio internazionale$emanuale teorico-pratico per la redazione dei contratti$fAldo Frignani$gcon la collaborazione di Marco Arato ... 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