Bayesian Modeling in Bioinformatics

Herausgeber: Dey, Dipak K.; Mallick, Bani K.; Ghosh, Samiran

• Broschiertes Buch

Produktbeschreibung

This volume 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. It presents a broad overview of statistical inference, clustering, and classification problems in two main high-throughput platforms: microarray gene expression and phylogenic analysis. Illustrating concepts using real-world data, the book covers a variety of recently developed Bayesian techniques, along with applications in genome-wide studies, phylogenetics, breast cancer, expression genomics, and more.

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Produktdetails

• Verlag: Chapman and Hall/CRC
• Seitenzahl: 468
• Erscheinungstermin: 17. Oktober 2019
• Englisch
• Abmessung: 234mm x 156mm x 25mm
• Gewicht: 705g
• ISBN-13: 9780367383657
• ISBN-10: 0367383659
• Artikelnr.: 58058766

Autorenporträt

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.

Inhaltsangabe

Estimation and Testing in Time-Course Microarray Experiments
Classification for Differential Gene Expression Using Bayesian Hierarchical Models
Applications of the Mode Oriented Stochastic Search (MOSS) for Discrete Multi-Way Data to Genome -Wide Studies
Nonparametric Bayesian Bioinformatics
Measurement Error Models for cDNA Microarray and Time-to-Event Data with Applications to Breast Cancer
Robust Inference for Differential Gene Expression
Hidden Markov Modeling of Array CGH Data
Recent Developments in Bayesian Phylogenetics
Gene Selection for the Identification of Biomarkers in High-Throughput Data
Sparsity Priors for Protein-Protein Interaction Predictions
Learning Bayesian Networks for Gene Expression Data
In Vitro to In Vivo Factor Profiling in Expression Genomics
Proportional Hazards Regression Using Bayesian Kernel Machines
Mixture Model for Protein Biomarker Discovery
and Bandopadhyay Bayesian Methods for Detecting Differentially Expressed and Empirical Bayes Methods for Spotted Microarray Data Bayesian Classification Method for QTL Mapping