Bayesian Analysis of Gene Expression Data
John Wiley & Sons, Jul 20, 2009 - Mathematics - 252 pages
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.
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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3 Bayesian Linear Models for Gene Expression
4 Bayesian Multiple Testing and False Discovery Rate Analysis
5 Bayesian Classification for Microarray Data
6 Bayesian Hypothesis Inference for Gene Classes
7 Unsupervised Classification and Bayesian Clustering