Bayesian Analysis of Gene Expression Data

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John Wiley & Sons, Jul 20, 2009 - Mathematics - 252 pages
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The field of high-throughput genetic experimentation is evolvingrapidly, with the advent of new technologies and new venues fordata mining. Bayesian methods play a role central to the future ofdata and knowledge integration in the field of Bioinformatics. Thisbook is devoted exclusively to Bayesian methods of analysis forapplications to high-throughput gene expression data, exploring therelevant 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 advancedmethods challenging and attainable.

This book:

  • Introduces the fundamentals in Bayesian methods of analysis forapplications to high-throughput gene expression data.
  • Provides an extensive review of Bayesian analysis and advancedtopics for Bioinformatics, including examples that extensivelydetail the necessary applications.
  • Accompanied by website featuring datasets, exercises andsolutions.

Bayesian Analysis of Gene Expression Data offers a uniqueintroduction to both Bayesian analysis and gene expression, aimedat graduate students in Statistics, Biomedical Engineers, ComputerScientists, Biostatisticians, Statistical Geneticists,Computational Biologists, applied Mathematicians and Medicalconsultants working in genomics. Bioinformatics researchers frommany fields will find much value in this book.

 

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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
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About the author (2009)

Bani Mallick, Department of Statistics, Texas A&MUniversity, USA.

Veera Balandandayuthapani, Department of Biostatistics,Anderson Cancer Center, Texas, USA.

David L. Gold, Department of Biostatistics, School of PublicHealth and Health Professions, University at Buffalo, The StateUniversity of New York, USA.

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