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Bayesian Inference for Gene Expression and Proteomics 1st Edition by Marina Vannucci ISBN 052186092X 9780521860925

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Bayesian Inference for Gene Expression and Proteomics 1st Edition Marina Vannucci Digital Instant Download

Author(s): Marina Vannucci
ISBN(s): 9780521860925, 052186092X
Edition: 1
File Details: PDF, 13.93 MB
Year: 2006
Language: english
SKU: EB-2011438 Category: Tag:

Bayesian Inference for Gene Expression and Proteomics 1st Edition by Marina Vannucci – Ebook PDF Instant Download/Delivery: 052186092X, 9780521860925
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ISBN 10: 052186092X 
ISBN 13: 9780521860925
Author: Marina Vannucci

The interdisciplinary nature of bioinformatics presents a research challenge in integrating concepts, methods, software and multiplatform data. Although there have been rapid developments in new technology and an inundation of statistical methods for addressing other types of high-throughput data, such as proteomic profiles that arise from mass spectrometry experiments. This book discusses the development and application of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as molecular and structural biology. The Bayesian approach has the advantage that evidence can be easily and flexibly incorporated into statistical methods. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation is followed by expert reviews of Bayesian methodology, tools and software for single group inference, group comparisons, classification and clustering, motif discovery and regulatory networks, and Bayesian networks and gene interactions.

Bayesian Inference for Gene Expression and Proteomics 1st Table of contents:

  • 1. An Introduction to High-Throughput Bioinformatics Data

    • By Keith A. Baggerly, Kevin R. Coombes, and Jeffrey S. Morris

  • 2. Hierarchical Mixture Models for Expression Profiles

    • By Michael A. Newton, Ping Wang, and Christina Kendziorski

  • 3. Bayesian Hierarchical Models for Inference in Microarray Data

    • By Anne-Mette K. Hein, Alex Lewin, and Sylvia Richardson

  • 4. Bayesian Process-Based Modeling of Two-Channel Microarray Experiments: Estimating Absolute mRNA Concentrations

    • By Mark A. van de Wiel, Marit Holden, Ingrid K. Glad, Heidi Lyng, and Arnoldo Frigessi

  • 5. Identification of Biomarkers in Classification and Clustering of High-Throughput Data

    • By Mahlet G. Tadesse, Naijun Sha, Sinae Kim, and Marina Vannucci

  • 6. Modeling Nonlinear Gene Interactions Using Bayesian MARS

    • By Veerabhadran Baladandayuthapani, Chris C. Holmes, Bani K. Mallick, and Raymond J. Carroll

  • 7. Models for Probability of Under- and Overexpression: The POE Scale

    • By Elizabeth Garrett-Mayer and Robert Scharpf

  • 8. Sparse Statistical Modelling in Gene Expression Genomics

    • By Joseph Lucas, Carlos Carvalho, Quanli Wang, Andrea Bild, Joseph R. Nevins, and Mike West

  • 9. Bayesian Analysis of Cell Cycle Gene Expression Data

    • By Chuan Zhou, Jon C. Wakefield, and Linda L. Breeden

  • 10. Model-Based Clustering for Expression Data via a Dirichlet Process Mixture Model

    • By David B. Dahl

  • 11. Interval Mapping for Expression Quantitative Trait Loci

    • By Meng Chen and Christina Kendziorski

  • 12. Bayesian Mixture Models for Gene Expression and Protein Profiles

    • By W. Evan Johnson and Brad Carlin

  • 13. Shrinkage Estimation for SAGE Data Using a Mixture Dirichlet Prior

    • By Hongzhe Li and Peiliang Qu

  • 14. Analysis of Mass Spectrometry Data Using Bayesian Wavelet-Based Functional Mixed Models

    • By J. S. Morris and P. G. Baladandayuthapani

  • 15. Nonparametric Models for Proteomic Peak Identification and Quantification

    • By Wen-Chi Tsai and M. Elizabeth D. G. Garrett-Mayer

  • 16. Bayesian Modeling and Inference for Sequence Motif Discovery

    • By Jun S. Liu and Jingjing Liang

  • 17. Identification of DNA Regulatory Motifs and Regulators by Integrating Gene Expression and Sequence Data

    • By Kevin K. H. Lee and Richard G. M. Britten

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Tags: Marina Vannucci, Bayesian, Gene