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.
General
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