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Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R : Order Restricted Analysis of Microarray Data

Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R : Order Restricted Analysis of Microarray Data

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This book focuses on the analysis of dose-response microarray data in pharmaceutical settings, the goal being to cover this important topic for early drug development experiments and to provide user-friendly R packages that can be used to analyze this ...

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Modeling Dose-response Microarray Data in Early Drug Development Experiments Using R
Modeling Dose-response Microarray Data in Early Drug Development Experiments Using R
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Modeling Dose-response Microarray Data in Early Drug Development
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Modeling Dose-response Microarray Data in Early Drug Development Experiments Using R (Paperback)
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Modeling Dose-Response Microarray Data in Early Drug Development Exper
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This book focuses on the analysis of dose-response microarray data in pharmaceutical settings, the goal being to cover this important topic for early drug development experiments and to provide user-friendly R packages that can be used to analyze this ...
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Modeling Dose-response Microarray Data in Early Drug Development Experiments Using R : Order Restricted Analysis of Microarray Data
Modeling Dose-response Microarray Data in Early Drug Development Experiments Using R : Order Restricted Analysis of Microarray Data
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Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R: Order-Restricted Analysis of Microarray Data
Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R: Order-Restricted Analysis of Microarray Data
Item 9783642240065
Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R: Order-Restricted Analysis of Microarray Data
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Product Info
This book focuses on the analysis of dose-response microarray data in pharmaceutical settings, the goal being to cover this important topic for early drug development experiments and to provide user-friendly R packages that can be used to analyze this data. It is intended for biostatisticians and bioinformaticians in the pharmaceutical industry, biologists, and biostatistics/bioinformatics graduate students.Part I of the book is an introduction, in which we discuss the dose-response setting and the problem of estimating normal means under order restrictions. In particular, we discuss the pooled-adjacent-violator (PAV) algorithm and isotonic regression, as well as inference under order restrictions and non-linear parametric models, which are used in the second part of the book.Part II is the core of the book, in which we focus on the analysis of dose-response microarray data. Methodological topics discussed include:• Multiplicity adjustment• Test statistics and procedures for the analysis of dose-response microarray data• Resampling-based inference and use of the SAM method for small-variance genes in the data• Identification and classification of dose-response curve shapes• Clustering of order-restricted (but not necessarily monotone) dose-response profiles• Gene set analysis to facilitate the interpretation of microarray results• Hierarchical Bayesian models and Bayesian variable selection• Non-linear models for dose-response microarray data• Multiple contrast tests• Multiple confidence intervals for selected parameters adjusted for the false coverage-statement rateAll methodological issues in the book are illustrated using real-world examples of dose-response microarray datasets from early drug development experiments.
Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R : Order Restricted Analysis of Microarray Data
General
ISBN

9783642240065

Fiction/Non-Fiction

Non-Fiction

Publisher

Springer Verlag

Pages

282

List Price

$64.95

Publication Date

08/26/2012

Release Status

In Print

Format

Paperback

Language

English

Measurements

Height: 9 Inches (US)

Width: 6 Inches (US)

Thickness: 0.5 Inches (US)

Unit Weight: 0.95 Pounds (US)

Series

Use R!

Editor

Amaratunga, Dhammika

Bijnens, Luc

Lin, Dan

Shkedy, Ziv

Yekutieli, Daniel

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