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

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

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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
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 Experiments Using R
Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R
Item 9783642240065
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 rate All methodological issues in the book are illustrated using real-world examples of dose-response microarray datasets from early drug development experiments.
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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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This concise primer (based on lectures given at summer schools on complex systems and on a masters degree course in complex systems modeling) will provide graduate students and newcomers to the field with the basic knowledge of the concepts and methods of statistical physics and its potential for application to interdisciplinary topics. Indeed, in recent years, statistical physics has begun to attract the interest of a broad community of researchers in the field of complex system sciences, ranging from biology to the social sciences, economics and computer science. More generally, a growing number of graduate students and researchers feel the need to learn some basic concepts and questions originating in other disciplines without necessarily having to master all of the corresponding technicalities and jargon. Generally speaking, the goals of statistical physics may be summarized as follows: on the one hand to study systems composed of a large number of interactingWith the aim of providing a deeper insight into possible mechanisms of biological self-organization, this thesis presents new approaches to describe the process of self-assembly and the impact of spatial organization on the function of membrane proteins, from a statistical physics point of view. It focuses on three important scenarios: the assembly of membrane proteins, the collective response of mechanosensitive channels and the function of the twin arginine translocation (Tat) system. Using methods from equilibrium and non-equilibrium statistical mechanics, general conclusions were drawn that demonstrate the importance of the protein-protein interactions. Namely, in the first part a general aggregation dynamics model is formulated, and used to show that fragmentation crucially affects the efficiency of the self-assembly process of proteins. In the second part, by mapping the membrane-mediated forces into a simplified many-body system, the dynamic and equilibrium behaviour of interacting mechanosensitive channels is derived, showing that protein agglomeration strongly impacts its desired function. The final part develops a model that incorporates both the agglomeration and transport function of the Tat system, thereby providing a comprehensive description of this self-organizing process.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 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
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 rate All methodological issues in the book are illustrated using real-world examples of dose-response microarray datasets from early drug development experiments.
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Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R: Order-Restricted Analysis of Microarray Data (2012 Edition) by Lin, Dan/ Shkedy, Ziv/ Yekutieli, Daniel [Paperback]
Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R: Order-Restricted Analysis of Microarray Data (2012 Edition) by Lin, Dan/ Shkedy, Ziv/ Yekutieli, Daniel [Paperback]
Item UBM9783642240065
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 rate All methodological issues in the book are illustrated using real-world examples of dose-response microarray datasets from early drug development experiments. *Author: Lin, Dan/ Shkedy, Ziv/ Yekutieli, Daniel *Series Title: Use R *Binding Type: Paperback *Number of Pages: 282 *Publication Date: 2012/08/26 *Language: English *Dimensions: 9.18 x 6.12 x 0.60 inches
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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 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 rate

All 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 (Paperback)
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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