Computational Toxicology: Volume II

Author:   Brad Reisfeld ,  Arthur N. Mayeno
Publisher:   Humana Press Inc.
Edition:   2013 ed.
Volume:   930
ISBN:  

9781627030588


Pages:   648
Publication Date:   21 October 2012
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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Computational Toxicology: Volume II


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Author:   Brad Reisfeld ,  Arthur N. Mayeno
Publisher:   Humana Press Inc.
Imprint:   Humana Press Inc.
Edition:   2013 ed.
Volume:   930
Dimensions:   Width: 17.80cm , Height: 4.30cm , Length: 25.40cm
Weight:   1.422kg
ISBN:  

9781627030588


ISBN 10:   1627030581
Pages:   648
Publication Date:   21 October 2012
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Methods 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.

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