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Bayesian Modeling in Bioinformatics (Paperback)
Loot Price: R1,984
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Bayesian Modeling in Bioinformatics (Paperback)
Series: Chapman & Hall/CRC Biostatistics Series
Expected to ship within 12 - 17 working days
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Bayesian Modeling in Bioinformatics discusses the development and
application of Bayesian statistical methods for the analysis of
high-throughput bioinformatics data arising from problems in
molecular and structural biology and disease-related medical
research, such as cancer. It presents a broad overview of
statistical inference, clustering, and classification problems in
two main high-throughput platforms: microarray gene expression and
phylogenic analysis. The book explores Bayesian techniques and
models for detecting differentially expressed genes, classifying
differential gene expression, and identifying biomarkers. It
develops novel Bayesian nonparametric approaches for bioinformatics
problems, measurement error and survival models for cDNA
microarrays, a Bayesian hidden Markov modeling approach for CGH
array data, Bayesian approaches for phylogenic analysis, sparsity
priors for protein-protein interaction predictions, and Bayesian
networks for gene expression data. The text also describes
applications of mode-oriented stochastic search algorithms, in
vitro to in vivo factor profiling, proportional hazards regression
using Bayesian kernel machines, and QTL mapping. Focusing on
design, statistical inference, and data analysis from a Bayesian
perspective, this volume explores statistical challenges in
bioinformatics data analysis and modeling and offers solutions to
these problems. It encourages readers to draw on the evolving
technologies and promote statistical development in this area of
bioinformatics.
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