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008			121025s2013 xxu| o |||| 0|eng d
020			_a9781627030595
024	7		_a10.1007/978-1-62703-059-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050			_aRA1193.4 _b2013 EB
245	0	0	_aComputational Toxicology : _bVolume II _cedited by Brad Reisfeld, Arthur N. Mayeno
250			_a1st edition 2013
264		1	_aTotowa, NJ _bHumana Press _c2013
300			_a1 recurso en línea (XI, 648 páginas) _b153 ilustraciones, 62 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aMethods in Molecular Biology _x1940-6029 _v930
505	0		_aMethods for Building QSARs -- Accessing and Using Chemical Databases -- From QSAR to QSIIR: Searching for Enhanced Computational Toxicology Models -- Mutagenicity, Carcinogenicity and Other Endpoints -- Classification Models for Safe Drug Molecules -- QSAR and Metabolic Assessment Tools in the Assessment of Genotoxicity -- Gene Expression Networks -- Construction of Cell Type-Specific Logic Models of Signaling Networks Using CellNetOptimizer -- Regulatory Networks -- Computational Reconstruction of Metabolic Networks from KEGG -- Biomarkers -- Biomarkers: Environmental Public Health Indicators -- Modeling for Regulatory Purposes (Risk and Safety Assessment) -- Developmental Toxicity Prediction -- Predictive Computational Toxicology to Support Drug Safety Assessment -- Developing a Practical Toxicogenomics Data Analysis System Utilizing Open-Source Software -- Systems Toxicology from Genes to Organs -- Agent Based Models of Cellular Systems -- Linear Algebra -- Ordinary Differential Equations -- On the Development and Validation of QSAR Models -- Principal Components Analysis -- Partial Least Square Methods: Partial Least Squares Correlation and Partial Least Square Regression -- Maximum Likelihood -- Bayesian Inference.
520			_aRapid advances in computer science, biology, chemistry, and other disciplines are enabling powerful new computational tools and models for toxicology and pharmacology. These computational tools hold tremendous promise for advancing science, from streamlining drug efficacy and safety testing, to increasing the efficiency and effectiveness of risk assessment for environmental chemicals. Computational Toxicology provides biomedical and quantitative scientists with essential background, context, examples, useful tips, and an overview of current developments in the field. Divided into four sections, Volume I covers a wide array of methodologies and topics. Opening with an introduction to the field of computational toxicology and its current and potential applications, the volume continues with 'best practices' in mathematical and computational modeling, followed by chemoinformatics and the use of computational techniques and databases to predict chemical properties and toxicity, as well as an overview of molecular dynamics.  The final section is a compilation of the key elements and main approaches used in pharmacokinetic and pharmacodynamic modeling, including the modeling of absorption, compartment and non-compartmental modeling, physiologically based pharmacokinetic modeling, interspecies extrapolation, and population effects. Written in the successful Methods in Molecular Biology™ series format where possible, chapters include introductions to their respective topics, lists of the materials and software tools used, methods, and notes on troubleshooting. Authoritative and easily accessible, Computational Toxicology will allow motivated readers to participate in this exciting field and undertake a diversity of realistic problems of interest.
988			_aSpringer_Protocols_2013
650		7	_2embne _9140328 _aToxicología
776	0	8	_iPrinted edition: _z9781627030601
776	0	8	_iPrinted edition: _z9781627030588
776	0	8	_iPrinted edition: _z9781493963263
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-62703-059-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b11/2023 _dz _eu _zSI