Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R : Order-Restricted Analysis of Microarray Data
This book focuses on the analysis of dose-response microarray data in pharmaceutical setting, the goal being to cover this important topic for early drug development and to provide user-friendly R packages that can be used to analyze dose-response microarray data. It is intended for biostatisticians...
में बचाया:
| अन्य लेखक: | , , , , |
|---|---|
| स्वरूप: | Livre numérique |
| भाषा: | Anglais |
| प्रकाशित: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| संस्करण: | 1st ed. 2012. |
| श्रृंखला: | Use R!
|
| ऑनलाइन पहुंच: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| टिप्पणी: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R, Texte imprimé, 9783642240065 • Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R, Texte imprimé, 9783642240089 • Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R, Texte imprimé, 9783642240065 |
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| 245 | 0 | 0 | |a Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R : |b Order-Restricted Analysis of Microarray Data |c edited by Dan Lin, Ziv Shkedy, Daniel Yekutieli, Dhammika Amaratunga, Luc Bijnens. |
| 250 | |a 1st ed. 2012. | ||
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Use R! |x 2197-5744 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a Introduction Part I: Dose-response Modeling: An Introduction Estimation Under Order Restrictions The Likelihood Ratio Test Part II: Dose-response Microarray Experiments Functional Genomic Dose-response Experiments Adjustment for Multiplicity Test for Trend Order Restricted Bisclusters Classification of Trends in Dose-response Microarray Experiments Using Information Theory Selection Methods Multiple Contrast Test Confidence Intervals for the Selected Parameters Case Study Using GUI in R: Gene Expression Analysis After Acute Treatment With Antipsychotics | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a This book focuses on the analysis of dose-response microarray data in pharmaceutical setting, the goal being to cover this important topic for early drug development and to provide user-friendly R packages that can be used to analyze dose-response microarray 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 the likelihood ratio test and non-linear parametric models, which are used in the second part of the book. Part II is the core of the book. Methodological topics discussed include: · Multiplicity adjustment · Test statistics and testing procedures for the analysis of dose-response microarray data · Resampling-based inference and use of the SAM method at the presence of 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 · Hierarchical Bayesian models and non-linear models for dose-response microarray data · Multiple contrast testsAll methodological issues in the book are illustrated using four real-world examples of dose-response microarray datasets from early drug development experiments | ||
| 700 | 1 | |a Lin, Dan. |4 edt | |
| 700 | 1 | |a Shkedy, Ziv. |4 edt | |
| 700 | 1 | |a Yekutieli, Daniel. |4 edt | |
| 700 | 1 | |a Amaratunga, Dhammika, |d 1956- |4 edt | |
| 700 | 1 | |a Bijnens, Luc. |4 edt | |
| 776 | 0 | |t Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R |b Texte imprimé |z 9783642240065 | |
| 776 | 0 | |t Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R |b Texte imprimé |z 9783642240089 | |
| 776 | 0 | |t Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R |b Texte imprimé |z 9783642240065 | |
| 856 | 4 | |q PDF |u https://doi.org/10.1007/978-3-642-24007-2 |z Accès sur la plateforme de l'éditeur | |
| 856 | 4 | |u https://revue-sommaire.istex.fr/ark:/67375/8Q1-NGJ2QKXD-X |z Accès sur la plateforme Istex | |
| 856 | 4 | |5 452349901:749575921 |u https://ezproxy.univ-orleans.fr/login?url=https://dx.doi.org/10.1007/978-3-642-24007-2 |z Accès Université d'Orléans | |
| 856 | 4 | |5 180339901:752324578 |u https://ezproxy.insa-cvl.fr/login?qurl=https://dx.doi.org/10.1007/978-3-642-24007-2 |z Accès INSA CVL | |
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