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Bayesian Modeling in Bioinformatics

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advanced Bayesian bioinformatics modeling
Bayesian modeling
Bayesian networks
Bayesian Variable Selection Procedure
BF
bioinformatics
biomarkers
Category=PBT
Category=PS
classification problems
clustering
Conjugate Priors
Copy Number States
DE Gene
Dirichlet Process
Dirichlet Process Priors
discovery
distribution
DNA Microarray
DPM Model
Empirical Bayes Methods
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
factor
false
False Discovery Rate
Full Conditional Distributions
gene expression
genomics
gibbs
Gibbs Sampling
hidden Markov modeling
hierarchical modeling
hierarchical models
high-throughput experiments
high-throughput genomics
kernel machines
MCMC
MCMC Sample
MCMC Sampler
MCMC Simulation
Measurement Error Model
medical research
Microarray Data
microarrays
Mixture Model
molecular biology
network
phylogenetics
posterior
Posterior Distribution
Posterior Distributions
PPI Probability
probability
protein interaction prediction
QTL Mapping
rate
Roc Curve
sampler
statistical inference
statistical inference methods
stochastic search algorithms
structural biology
survival analysis techniques

Product details

  • ISBN 9780367383657
  • Weight: 453g
  • Dimensions: 156 x 234mm
  • Publication Date: 17 Oct 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Description

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.
About

Dipak K. Dey is a professor and head of the Department of Statistics at the University of Connecticut.

Samiran Ghosh is an assistant professor in the Department of Mathematical Sciences at Indiana University-Purdue University.

Bani K. Mallick is a professor of statistics and director of the Bayesian Bioinformatics Laboratory at Texas A&M University.