Titlebook: Computational Toxicology; Volume II Brad Reisfeld,Arthur N. Mayeno Book 2013 Springer Science+Business Media, LLC 2013 QSAR.biological netw

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Classification Models for Safe Drug Moleculesough there is an increasing use of classification models for prediction of either biological activity or toxicity, the future trend will naturally be towards the development of classification models capable of simultaneous prediction of biological activity, toxicity, and pharmacokinetic parameters s

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Gene Expression Networksression or protein–protein interaction networks. Also, approaches to reverse engineer gene networks or methods that seek to identify novel interactions between genes are described. Given the relatively small sample numbers typically available, these reverse engineering approaches are generally usefu

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Biomarkersargets with perturbed biological pathways. Two examples are given in this chapter: (1) inferring potential human biomarkers from animal toxicogenomics data, and (2) the identification of protein targets through computational means and associating these in one example with potential drug interactions

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1064-3745 pproaches used in the analysis of gene, signaling, regulatory, and metabolic networks. The next section focuses on diagnostic and prognostic molecular indicators (biomarkers), fol978-1-4939-6326-3978-1-62703-059-5Series ISSN 1064-3745 Series E-ISSN 1940-6029

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Accessing and Using Chemical Databasesed—identification, structural, and associated data. Functionality of chemical databases is presented, with examples of search and access types. More details are included about the OASIS database and platform and the Danish (Q)SAR Database online. Various types of chemical database resources are discussed, together with a list of examples.

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