Bayesian modeling in bioinformatics
Author(s)
Bibliographic Information
Bayesian modeling in bioinformatics
(Chapman & Hall/CRC biostatistics series, 34)
CRC Press, c2011
Available at 7 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references and index
Description and Table of Contents
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.
Table of Contents
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
by "Nielsen BookData"