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portada modeling dose-response microarray data in early drug development experiments using r
Type
Physical Book
Publisher
Language
Inglés
Pages
289
Format
Paperback
Dimensions
23.3 x 15.5 x 1.5 cm
Weight
0.42 kg.
ISBN
3642240062
ISBN13
9783642240065

modeling dose-response microarray data in early drug development experiments using r

Dan Lin (Illustrated by) · Ziv Shkedy (Illustrated by) · Daniel Yekutieli (Illustrated by) · Springer · Paperback

modeling dose-response microarray data in early drug development experiments using r - Lin, Dan ; Shkedy, Ziv ; Yekutieli, Daniel

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Synopsis "modeling dose-response microarray data in early drug development experiments using r"

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.

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