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.…mehr
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.
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
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
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