Bayesian Analysis of Gene Expression Data

Front Cover
John Wiley & Sons, Jul 20, 2009 - Mathematics - 252 pages
0 Reviews
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.

 

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

Table of Notation
xi
1 Bioinformatics and Gene Expression Experiments
1
2 Gene Expression Data Basic Biology and Experiments
5
3 Bayesian Linear Models for Gene Expression
21
4 Bayesian Multiple Testing and False Discovery Rate Analysis
51
5 Bayesian Classification for Microarray Data
69
6 Bayesian Hypothesis Inference for Gene Classes
89
7 Unsupervised Classification and Bayesian Clustering
109
8 Bayesian Graphical Models
137
9 Advanced Topics
151
Appendix A Basics of Bayesian Modeling
159
Appendix B Bayesian Computation Tools
185
References
217
Index
237
Copyright

Other editions - View all

Common terms and phrases

About the author (2009)

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.

Bibliographic information