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

• Record Nr.: UNINA9910830664603321
• 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

Statistical methods for spatial planning and monitoring / / Silvestro Montrone, Paola Perchinunno, editors

• Record Nr.: UNINA9910438147603321
• Pubbl/distr/stampa: Milano, : Springer, 2012
• ISBN: 1-283-74096-6; 88-470-2751-9
• Edizione: [1st ed. 2013.]
• Descrizione fisica: 1 online resource (166 p.)
• Collana: Contributions to statistics, , 1431-1968
• Altri autori (Persone): MontroneSilvestro; PerchinunnoPaola
• Disciplina: 519.5
• Soggetti: Spatial analysis (Statistics); Mathematical statistics
• 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: 1. Geographical Disparities in Mortality Rates: Spatial Data Mining and Bayesian Hierarchical Modeling -- 2. A Fuzzy Approach to Ward’s Method of Classification: an Application Case To the Italian University System -- 3. Geostatistics and the Role of Variogram in Time Series Analysis: a Critical Review -- 4. Geostatistics and GIS: Tools for Environmental Risk Assessment -- 5. Socio-Economic Zoning: Comparing Two Statistical Methods -- 6. A Geostatistical Approach to Measure Shrinking Cities: the Case of Taranto -- 7. Social Identity as Determinant of Real Estate Economy in Manhattan. .
• Sommario/riassunto: The book aims to investigate methods and techniques for spatial statistical analysis suitable to model spatial information in support of decision systems. Over the last few years there has been a considerable interest in these tools and in the role they can play in spatial planning and environmental modelling. One of the earliest and most famous definition of spatial planning was “a geographical expression to the economic, social, cultural and ecological policies of society”: borrowing from this point of view, this text shows how an interdisciplinary approach is an effective way to an harmonious integration of national policies with regional and local analysis. A wide range of spatial models and techniques is, also, covered: spatial data mining, point processes ; analysis, nearest neighbor statistics and cluster detection, Fuzzy Regression model and local indicators of spatial association; all of these tools provide the policy-maker with a valuable support to policy development.