LEADER 05488nam 2200673Ia 450 001 9910877239703321 005 20200520144314.0 010 $a1-282-34942-2 010 $a9786612349423 010 $a0-470-74278-X 010 $a0-470-74281-X 035 $a(CKB)1000000000788733 035 $a(EBL)454284 035 $a(OCoLC)436149759 035 $a(SSID)ssj0000109291 035 $a(PQKBManifestationID)11138079 035 $a(PQKBTitleCode)TC0000109291 035 $a(PQKBWorkID)10045413 035 $a(PQKB)10035505 035 $a(MiAaPQ)EBC454284 035 $a(PPN)144328739 035 $a(EXLCZ)991000000000788733 100 $a20090605d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aBayesian analysis of gene expression data /$fedited by Bani Mallick, David Gold, and Veera Baladandayuthapani 210 $aHoboken, N.J. $cWiley$d2009 215 $a1 online resource (254 p.) 225 1 $aStatistics in practice. 300 $aDescription based upon print version of record. 311 $a0-470-51766-2 320 $aIncludes bibliographical references and index. 327 $aBayesian Analysis of Gene Expression Data; Contents; Table of Notation; 1 Bioinformatics and Gene Expression Experiments; 1.1 Introduction; 1.2 About This Book; 2 Gene Expression Data: Basic Biology and Experiments; 2.1 Background Biology; 2.1.1 DNA Structures and Transcription; 2.2 Gene Expression Microarray Experiments; 2.2.1 Microarray Designs; 2.2.2 Work Flow; 2.2.3 Data Cleaning; 3 Bayesian Linear Models for Gene Expression; 3.1 Introduction; 3.2 Bayesian Analysis of a Linear Model; 3.2.1 Analysis via Conjugate Priors; 3.2.2 Bayesian Variable Selection; 3.2.3 Model Selection Priors 327 $a3.2.4 Priors on Regression Coefficients3.2.5 Sparsity Priors; 3.3 Bayesian Linear Models for Differential Expression; 3.3.1 Relevant Work; 3.4 Bayesian ANOVA for Gene Selection; 3.4.1 The Basic Bayesian ANOVA Model; 3.4.2 Differential Expression via Model Selection; 3.5 Robust ANOVA model with Mixtures of Singular Distributions; 3.6 Case Study; 3.7 Accounting for Nuisance Effects; 3.8 Summary and Further Reading; 4 Bayesian Multiple Testing and False Discovery Rate Analysis; 4.1 Introduction to Multiple Testing; 4.2 False Discovery Rate Analysis; 4.2.1 Theoretical Developments 327 $a4.2.2 FDR Analysis with Gene Expression Arrays4.3 Bayesian False Discovery Rate Analysis; 4.3.1 Theoretical Developments; 4.4 Bayesian Estimation of FDR; 4.5 FDR and Decision Theory; 4.6 FDR and bFDR Summary; 5 Bayesian Classification for Microarray Data; 5.1 Introduction; 5.2 Classification and Discriminant Rules; 5.3 Bayesian Discriminant Analysis; 5.4 Bayesian Regression Based Approaches to Classification; 5.4.1 Bayesian Analysis of Generalized Linear Models; 5.4.2 Link Functions; 5.4.3 GLM using Latent Processes; 5.4.4 Priors and Computation 327 $a5.4.5 Bayesian Probit Regression using Auxiliary Variables5.5 Bayesian Nonlinear Classification; 5.5.1 Classification using Interactions; 5.5.2 Classification using Kernel Methods; 5.6 Prediction and Model Choice; 5.7 Examples; 5.8 Discussion; 6 Bayesian Hypothesis Inference for Gene Classes; 6.1 Interpreting Microarray Results; 6.2 Gene Classes; 6.2.1 Enrichment Analysis; 6.3 Bayesian Enrichment Analysis; 6.4 Multivariate Gene Class Detection; 6.4.1 Extending the Bayesian ANOVA Model; 6.4.2 Bayesian Decomposition; 6.5 Summary; 7 Unsupervised Classification and Bayesian Clustering 327 $a7.1 Introduction to Bayesian Clustering for Gene Expression Data7.2 Hierarchical Clustering; 7.3 K-Means Clustering; 7.4 Model-Based Clustering; 7.5 Model-Based Agglomerative Hierarchical Clustering; 7.6 Bayesian Clustering; 7.7 Principal Components; 7.8 Mixture Modeling; 7.8.1 Label Switching; 7.9 Clustering Using Dirichlet Process Prior; 7.9.1 Infinite Mixture of Gaussian Distributions; 8 Bayesian Graphical Models; 8.1 Introduction; 8.2 Probabilistic Graphical Models; 8.3 Bayesian Networks; 8.4 Inference for Network Models; 8.4.1 Multinomial-Dirichlet Model; 8.4.2 Gaussian Model 327 $a8.4.3 Model Search 330 $aThe field of high-throughput genetic experimentation is evolving rapidly, with the advent of new technologies and new venues for data mining. Bayesian methods play a role central to the future of data and knowledge integration in the field of Bioinformatics. This book is devoted exclusively to Bayesian methods of analysis for applications to high-throughput gene expression data, exploring the relevant methods that are changing Bioinformatics. Case studies, illustrating Bayesian analyses of public gene expression data, provide the backdrop for students to develop analytical skills, while the mo 410 0$aStatistics in practice. 606 $aGene expression$xStatistical methods 606 $aBayesian statistical decision theory 615 0$aGene expression$xStatistical methods. 615 0$aBayesian statistical decision theory. 676 $a572.8 676 $a572.86501519542 701 $aMallick$b Bani K.$f1965-$0771098 701 $aGold$b David$f1970-$01759682 701 $aBaladandayuthapani$b Veerabhadran$f1976-$01759683 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910877239703321 996 $aBayesian analysis of gene expression data$94198289 997 $aUNINA