05594nam 22006854a 450 991014608220332120170815114555.01-280-25332-097866102533260-470-35030-X0-471-72612-50-471-72842-X(CKB)1000000000018957(EBL)226437(OCoLC)475932606(SSID)ssj0000103128(PQKBManifestationID)11120156(PQKBTitleCode)TC0000103128(PQKBWorkID)10060574(PQKB)10929027(MiAaPQ)EBC226437(EXLCZ)99100000000001895720040503d2004 uy 0engur|n|---|||||txtccrAnalyzing microarray gene expression data[electronic resource] /Geoffrey J. McLachlan, Kim-Anh Do, Christopher AmbroiseHoboken, N.J. Wiley-Intersciencec20041 online resource (366 p.)Wiley series in probability and statisticsDescription based upon print version of record.0-471-22616-5 Includes bibliographical references and index.Analyzing Microarray Gene Expression Data; Contents; Preface; 1 Microarrays in Gene Expression Studies; 1.1 Introduction; 1.2 Background Biology; 1.2.1 Genome, Genotype, and Gene Expression; 1.2.2 Of Wild-Types and Other Alleles; 1.2.3 Aspects of Underlying Biology and Physiochemistry; 1.3 Polymerase Chain Reaction; 1.4 cDNA; 1.4.1 Expressed Sequence Tag; 1.5 Microarray Technology and Application; 1.5.1 History of Microarray Development; 1.5.2 Tools of Microarray Technology; 1.5.3 Limitations of Microarray Technology; 1.5.4 Oligonucleotides versus cDNA Arrays1.5.5 SAGE: Another Method for Detecting and Measuring Gene Expression Levels1.5.6 Emerging Technologies; 1.6 Sampling of Relevant Research Entities and Public Resources; 2 Cleaning and Normalization; 2.1 Introduction; 2.2 Cleaning Procedures; 2.2.1 Image Processing to Extract Information; 2.2.2 Missing Value Estimation; 2.2.3 Sources of Nonlinearity; 2.3 Normalization and Plotting Procedures for Oligonucleotide Arrays; 2.3.1 Global Approaches for Oligonucleotide Array Data; 2.3.2 Spiked Standard Approaches; 2.3.3 Geometric Mean and Linear Regression Normalization for Multiple Arrays2.3.4 Nonlinear Normalization for Multiple Arrays Using Smooth Curves2.4 Normalization Methods for cDNA Microarray Data; 2.4.1 Single-Array Normalization; 2.4.2 Multiple Slides Normalization; 2.4.3 ANOVA and Related Methods for Normalization; 2.4.4 Mixed-Model Method for Normalization; 2.4.5 SNOMAD; 2.5 Transformations and Replication; 2.5.1 Importance of Replication; 2.5.2 Transformations; 2.6 Analysis of the Alon Data Set; 2.7 Comparison of Normalization Strategies and Discussion; 3 Some Cluster Analysis Methods; 3.1 Introduction; 3.2 Reduction in the Dimension of the Feature Space3.3 Cluster Analysis3.4 Some Hierarchical Agglomerative Techniques; 3.5 k-Means Clustering; 3.6 Cluster Analysis with No A Priori Metric; 3.7 Clustering via Finite Mixture Models; 3.7.1 Definition; 3.7.2 Advantages of Model-Based Clustering; 3.8 Fitting Mixture Models Via the EM Algorithm; 3.8.1 E-Step; 3.8.2 M-Step; 3.8.3 Choice of Starting Values for the EM Algorithm; 3.9 Clustering Via Normal Mixtures; 3.9.1 Heteroscedastic Components; 3.9.2 Homoscedastic Components; 3.9.3 Spherical Components; 3.9.4 Choice of Root; 3.9.5 Available Software; 3.10 Mixtures of t Distributions3.11 Mixtures of Factor Analyzers3.12 Choice of Clustering Solution; 3.13 Classification ML Approach; 3.14 Mixture Models for Clinical and Microarray Data; 3.14.1 Unconditional Approach; 3.14.2 Conditional Approach; 3.15 Choice of the Number of Components in a Mixture Model; 3.15.1 Order of a Mixture Model; 3.15.2 Approaches for Assessing Mixture Order; 3.15.3 Bayesian Information Criterion; 3.15.4 Integrated Classification Likelihood Criterion; 3.16 Resampling Approach; 3.17 Other Resampling Approaches for Number of Clusters; 3.17.1 The Gap Statistic3.17.2 The Clest Method for the Number of ClustersA multi-discipline, hands-on guide to microarray analysis of biological processes Analyzing Microarray Gene Expression Data provides a comprehensive review of available methodologies for the analysis of data derived from the latest DNA microarray technologies. Designed for biostatisticians entering the field of microarray analysis as well as biologists seeking to more effectively analyze their own experimental data, the text features a unique interdisciplinary approach and a combined academic and practical perspective that offers readers the most complete and applied coverage of the subject Wiley series in probability and statistics.DNA microarraysStatistical methodsGene expressionStatistical methodsElectronic books.DNA microarraysStatistical methods.Gene expressionStatistical methods.572.8636572.865McLachlan Geoffrey J.1946-27687Do Kim-Anh1960-1000608Ambroise Christophe1969-1000609MiAaPQMiAaPQMiAaPQBOOK9910146082203321Analyzing microarray gene expression data2296640UNINA04926oam 22010814 450 991016028320332120250426110454.09781498381840149838184797814843466171484346610(CKB)3710000001025864(BIP)077136978(BIP)077145634(IMF)SDNAA201410SDNAA201410(EXLCZ)99371000000102586420020129d2014 uf 0aratxtrdacontentcrdamediacrrdacarrierMaking the Most of Public Investment in MENA and CCA Oil-Exporting Countries /Maria Albino, Svetlana Cerovic, Francesco Grigoli, Juan Flores, Javier Kapsoli, Haonan Qu, Yahia Said, Bahrom Shukurov, Martin Sommer, SeokHyun YoonWashington, D.C. :International Monetary Fund,2014.1 online resource (31 p.)Staff Discussion Notes9781498314954 1498314953 Over the past decade, rising oil prices have translated into high levels of public investment in most MENA and CCA oil exporters. This has prompted questions about the efficiency of public investment in generating growth and closing infrastructure gaps, as well as concerns about fiscal vulnerabilities. When public investment is inefficient, higher levels of spending may simply lead to larger budget deficits, without sufficiency increasing the quantity or quality of public infrastructure in support of economic growth. This paper examines the efficiency of public investment in the MENA and CCA oil exporters using several techniques, including a novel application of the efficiency frontier analysis, estimates of unit investment costs, and assessments of public investment processes. The analysis confirms that these oil exporters have substantial room to improve public investment efficiency. Reforms in the public financial and investment management systems are needed to achieve this objective.Staff Discussion Notes; Staff Discussion Notes ;No. 2014/010Agricultural and Natural Resource EconomicsimfCapacityimfCapital investmentsimfCapital spendingimfCapitalimfEnvironmental and Ecological Economics: GeneralimfEnvironmental managementimfExhaustible Resources and Economic DevelopmentimfFiscal and Monetary Policy in DevelopmentimfFiscal PolicyimfInfrastructureimfIntangible CapitalimfInvestmentimfMacroeconomicsimfNational Government Expenditures and Related Policies: InfrastructuresimfNatural ResourcesimfNatural resourcesimfOther Public Investment and Capital StockimfPublic finance & taxationimfPublic FinanceimfPublic investment and public-private partnerships (PPP)imfPublic investment spendingimfPublic investmentsimfPublic-private sector cooperationimfSaving and investmentimfUnited StatesimfAgricultural and Natural Resource EconomicsCapacityCapital investmentsCapital spendingCapitalEnvironmental and Ecological Economics: GeneralEnvironmental managementExhaustible Resources and Economic DevelopmentFiscal and Monetary Policy in DevelopmentFiscal PolicyInfrastructureIntangible CapitalInvestmentMacroeconomicsNational Government Expenditures and Related Policies: InfrastructuresNatural ResourcesNatural resourcesOther Public Investment and Capital StockPublic finance & taxationPublic FinancePublic investment and public-private partnerships (PPP)Public investment spendingPublic investmentsPublic-private sector cooperationSaving and investmentAlbino Maria1770359Cerovic Svetlana1211197Flores Juan192603Grigoli Francesco1127643Kapsoli Javier1770360Qu Haonan1770361Said Yahia1770362DcWaIMFBOOK9910160283203321Making the Most of Public Investment in MENA and CCA Oil-Exporting Countries4250544UNINA