000 04071nam a22004215i 4500
001 303667
003 MX-SnUAN
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008 150903s2012 gw | o |||| 0|eng d
020 _a9783642240072
_99783642240072
024 7 _a10.1007/9783642240072
_2doi
035 _avtls000357915
039 9 _a201509030527
_bVLOAD
_c201405070223
_dVLOAD
_y201402191510
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aQA276-280
100 1 _aLin, Dan.
_eeditor.
_9342308
245 1 0 _aModeling Dose-Response Microarray Data in Early Drug Development Experiments Using R :
_bOrder-Restricted Analysis of Microarray Data /
_cedited by Dan Lin, Ziv Shkedy, Daniel Yekutieli, Dhammika Amaratunga, Luc Bijnens.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2012.
300 _axv, 282 páginas 96 ilustraciones, 4 ilustraciones en color.
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aUse R!
500 _aSpringer eBooks
505 0 _aIntroduction -- 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.
520 _aThis 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 tests All methodological issues in the book are illustrated using four “real-world” examples of dose-response microarray datasets from early drug development experiments.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aShkedy, Ziv.
_eeditor.
_9318390
700 1 _aYekutieli, Daniel.
_eeditor.
_9342309
700 1 _aAmaratunga, Dhammika.
_eeditor.
_9342310
700 1 _aBijnens, Luc.
_eeditor.
_9342311
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9783642240065
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-642-24007-2
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c303667
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