05560nam 22006734a 450 991083063780332120230617031222.01-280-25332-097866102533260-470-35030-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 methodsDNA microarraysStatistical methods.Gene expressionStatistical methods.572.8636572.865McLachlan Geoffrey J.1946-27687Do Kim-Anh1960-1612576Ambroise Christophe1969-1612577MiAaPQMiAaPQMiAaPQBOOK9910830637803321Analyzing microarray gene expression data3941458UNINA04961nam 2201213z- 450 991055761620332120220321(CKB)5400000000045239(oapen)https://directory.doabooks.org/handle/20.500.12854/79646(oapen)doab79646(EXLCZ)99540000000004523920202203d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierStudy of the Influence of Abiotic and Biotic Stress Factors on Horticultural PlantsBaselMDPI - Multidisciplinary Digital Publishing Institute20221 online resource (220 p.)3-0365-3112-2 3-0365-3113-0 We would like to provide the scientists a set of studies entitled "Study of the Influence of Abiotic and Biotic Stress Factors on Horticultural Plants". The reprint book contains 12 papers about the influence of the stress factors on the plant growth and soil parameters. Authors descripted the impact of the biotic and abiotic stress factors (i.e., high, and low temperature, salt, inorganic pollutants such as salts, heavy metals, phosphite, as well as irrigation) on the physiological, biochemical, and anatomical changes occurring in the plants at the cellular, tissue, organ, and whole plant level. The subject of these studies were different plant species, i.e., watermelon, lettuce, kale, potato, grapevine, hops, orchid, strawberry, and boxwood. The ideas of the papers can be divided into five topics: (1) achieving better quality of plant material for food production by changes made in the growth conditions, metabolic and genetic modifications; (2) increasing the plant resistance to environmental stresses by application of exogenous compounds of different chemical character; (3) reducing plant stress caused by anthropogenic activity applying nonmodified and genetically modified plants; (4) mitigating drought stress by irrigation; and 5) the positive effect of plant growth-promoting microorganisms on horticulture plants performance during drought stress.Biology, life sciencesbicsscResearch and information: generalbicssc5-aminolevulinic acidabiotic stressadaptive responsesanti-oxidantsantioxidant enzymeantioxidant enzymesbiopreparationsBrassica oleracea var. acephalaBuxus megistophyllachitosan (CTS)chlorophyll fast fluorescence characteristicsclimate changecold stresscold-responsive genescompanion plantsdaily increasedisease-resistant varietiesdowny mildewdrought stressfly ashfruit compositiongene expressiongenotypesgrapevinehop ridgeshormone profilingHumulus lupulus L.hydrogen peroxideleaf mesostructureleaf relative water contentlettucelimingmalic acidmalondialdehydemaximum daily shrinkagemetalsmineral nutritionn/aorchidphosphite stressphotosynthesisphotosynthetic pigmentsphytochemicalsphytoremediationpigmentspiwi cultivarsplant growth-promoting microorganismsplant introductionplant stimulationpotatoprolineripeningroot morphologyrootstocksalinityshort-term cold stresssignal intensitysoil bulk densitysoil porositysoluble sugarsstem water potentialstrawberrytransformed ecosystemsurban road greeningVitis spp.water exchangewatermelonBiology, life sciencesResearch and information: generalHanaka Agnieszkaedt1295553Jaroszuk-Ściseł JolantaedtMajewska MałgorzataedtHanaka AgnieszkaothJaroszuk-Ściseł JolantaothMajewska MałgorzataothBOOK9910557616203321Study of the Influence of Abiotic and Biotic Stress Factors on Horticultural Plants3023611UNINA