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003			ES-MaUEC
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007			cr nn 008mamaa
008			230207s2023 sz | o |||| 0|eng d
020			_a9783031207303
024	7		_a10.1007/978-3-031-20730-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRA1193.4 _b2023 EB
245	0	0	_aMachine Learning and Deep Learning in Computational Toxicology _cedited by Huixiao Hong
250			_a1st ed. 2023
264		1	_aCham _bSpringer International Publishing _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aComputational Methods in Engineering & the Sciences _x2662-4877
505	0		_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. .
520			_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. .
988			_aSpringer_BiomedLife_2023
650		7	_2embne _9140328 _aToxicología _xProceso de datos
650		7	_2embne _9166090 _aAprendizaje automático
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-20730-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2024 _dz _eIG _zSI