1: Manuale / Louis Corman

• Record Nr.: UNISOBSOBE00039584
• Autore: Corman, Louis
• Pubbl/distr/stampa: Torino : Boringhieri, 1973
• Descrizione fisica: 250 p. ; 20 cm
• Collana: Testi e manuali della scienza contemporanea : Serie di psicologia e psichiatria
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Bayesian analysis of gene expression data [[electronic resource] /] / edited by Bani Mallick, David Gold, and Veera Baladandayuthapani

• Record Nr.: UNINA9910139785403321
• Autore: Mallick Bani K. <1965->
• Pubbl/distr/stampa: Hoboken, N.J., : Wiley, 2009
• ISBN: 1-282-34942-2; 9786612349423; 0-470-74278-X; 0-470-74281-X
• Descrizione fisica: 1 online resource (254 p.)
• Collana: Statistics in practice.
• Altri autori (Persone): MallickBani K. <1965->; GoldDavid <1970->; BaladandayuthapaniVeerabhadran <1976->
• Disciplina: 572.8; 572.86501519542
• Soggetti: Gene expression - Statistical methods; Bayesian statistical decision theory
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Bayesian 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; 3.2.4 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; 4.2.2 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; 5.4.5 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; 7.1 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; 8.4.3 Model Search
• Sommario/riassunto: The 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