modeling dose response microarray data (5 Ergebnisse)

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Paperback. Zustand: Brand New. 2012 edition. 297 pages. 9.00x6.00x0.50 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Taschenbuch. Zustand: Neu. Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R | Order-Restricted Analysis of Microarray Data | Dan Lin (u. a.) | Taschenbuch | xv | Englisch | 2012 | Springer | EAN 9783642240065 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Zustand: Gut. Zustand: Gut | Seiten: 300 | Sprache: Englisch | Produktart: Bücher | 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.