000			04320nam a22005895i 4500
001			978-3-642-24007-2
003			DE-He213
005			20140220083302.0
007			cr nn 008mamaa
008			120824s2012 gw | s |||| 0|eng d
020			_a9783642240072 _9978-3-642-24007-2
024	7		_a10.1007/978-3-642-24007-2 _2doi
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aLin, Dan. _eeditor.
245	1	0	_aModeling Dose-Response Microarray Data in Early Drug Development Experiments Using R _h[electronic resource] : _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 p. 96 illus., 4 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aUse R!
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.
650		0	_aStatistics.
650		0	_aPharmaceutical technology.
650		0	_aBioinformatics.
650		0	_aStatistical methods.
650		0	_aBiology _xData processing.
650		0	_aMathematical statistics.
650	1	4	_aStatistics.
650	2	4	_aStatistics, general.
650	2	4	_aStatistics and Computing/Statistics Programs.
650	2	4	_aPharmaceutical Sciences/Technology.
650	2	4	_aBiostatistics.
650	2	4	_aBioinformatics.
650	2	4	_aComputer Appl. in Life Sciences.
700	1		_aShkedy, Ziv. _eeditor.
700	1		_aYekutieli, Daniel. _eeditor.
700	1		_aAmaratunga, Dhammika. _eeditor.
700	1		_aBijnens, Luc. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642240065
830		0	_aUse R!
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-24007-2
912			_aZDB-2-SMA
999			_c102248 _d102248