Bani K. Mallick, David Gold, Veera Baladandayuthapani

Bayesian Analysis of Gene Expression Data (eBook, PDF)

• Format: PDF

Produktbeschreibung

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 more experienced readers will find the review of advanced methods challenging and attainable. This book: * Introduces the fundamentals in Bayesian methods of analysis for applications to high-throughput gene expression data. * Provides an extensive review of Bayesian analysis and advanced topics for Bioinformatics, including examples that extensively detail the necessary applications. * Accompanied by website featuring datasets, exercises and solutions. Bayesian Analysis of Gene Expression Data offers a unique introduction to both Bayesian analysis and gene expression, aimed at graduate students in Statistics, Biomedical Engineers, Computer Scientists, Biostatisticians, Statistical Geneticists, Computational Biologists, applied Mathematicians and Medical consultants working in genomics. Bioinformatics researchers from many fields will find much value in this book.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 252
• Erscheinungstermin: 6. Juli 2009
• Englisch
• ISBN-13: 9780470742815
• Artikelnr.: 37299065

Autorenporträt

Bani Mallick, Department of Statistics, Texas A&M University, USA. Veera Balandandayuthapani, Department of Biostatistics, Anderson Cancer Center, Texas, USA. David L. Gold, Department of Biostatistics, School of Public Health and Health Professions, University at Buffalo, The State University of New York, USA.

Inhaltsangabe

Table of Notation. 1 Bioinformatics and Gene Expression Experiments. 1.1
Introduction. 1.2 About This Book. 2 Basic Biology. 2.1 Background. 2.1.1
DNA Structures and Transcription. 2.2 Gene Expression Microarray
Experiments. 3 Bayesian Linear Models for Gene Expression. 3.1
Introduction. 3.2 Bayesian Analysis of a Linear Model. 3.3 Bayesian Linear
Models for Differential Expression. 3.4 Bayesian ANOVA for Gene 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. 4.3
Bayesian False Discovery Rate Analysis. 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. 5.5 Bayesian Nonlinear
Classification. 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. 6.3 Bayesian Enrichment
Analysis. 6.4 Multivariate Gene Class Detection. 6.5 Summary. 7
Unsupervised Classification and Bayesian Clustering. 7.1 Introduction to
Bayesian Clustering for Gene Expression Data. 7.2 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. 9 Advanced Topics. 9.1 Introduction. 9.2 Analysis of Time
Course Gene Expression Data. 9.3 Survival Prediction Using Gene Expression
Data. Appendix A: Basics of Bayesian Modeling. A.1 Basics. A.1.1 The
General Representation Theorem. A.1.2 Bayes' Theorem. A.1.3 Models Based on
Partial Exchangeability. A.1.4 Modeling with Predictors. A.1.5 Prior
Distributions. A.1.6 Decision Theory and Posterior and Predictive
Inferences. A.1.7 Predictive Distributions. A.1.8 Examples. A.2 Bayesian
Model Choice. A.3 Hierarchical Modeling. A.4 Bayesian Mixture Modeling. A.5
Bayesian Model Averaging. Appendix B: Bayesian Computation Tools. B.1
Overview. B.2 Large-Sample Posterior Approximations. B.2.1 The Bayesian
Central Limit Theorem. B.2.2 Laplace's Method. B.3 Monte Carlo Integration.
B.4 Importance Sampling. B.5 Rejection Sampling. B.6 Gibbs Sampling. B.7
The Metropolis Algorithm and Metropolis-Hastings. B.8 Advanced
Computational Methods. B.8.1 Block MCMC. B.8.2 Truncated Posterior Spaces.
B.8.3 Latent Variables and the Auto-Probit Model. B.8.4 Bayesian
Simultaneous Credible Envelopes. B.8.5 Proposal Updating. B.9 Posterior
Convergence Diagnostics. B.10 MCMC Convergence and the Proposal. B.10.1
Graphical Checks for MCMC Methods. B.10.2 Convergence Statistics. B.10.3
MCMC in High-Throughput Analysis. B.11 Summary. References. Index.

Rezensionen

"The target audience for this book is clearly statisticians rather than biologists ... It does provide a very useful overview of statistical genomics for anyone working in the field." (The Quarterly Review of Biology, 1 March 2012)

"Bioinformatics researchers from many fields will find much value in this book." (Mathematical Reviews, 2011)

"Experienced readers will find the review of advanced methods for bioinformatics challenging and attainable. This book will interest graduate students in statistics and bioinformatics researchers from many fields." (Book News, December 2009)