Bayesian inference for gene expression and proteomics

Bayesian inference for gene expression and proteomics

by Do, Kim-Anh Müller, Peter Vannucci, Marina

Type Book
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Title Statements

  • Full title :Bayesian inference for gene expression and proteomics / edited by Kim-Anh Do, Peter Müller, Marina Vannucci

Edition Statements

  • Publication Date:2006

Publication Statements

The Physical Object

  • Description:xviii, 437 s. : : illustrations (some color) ; : 24 cm

Edition Identifiers

  • ISBN:9780521860925, 052186092X
  • oclc:ocn476602792
  • LC Classification:QH450
  • Dewey Decimal:572.8 6501519542

Table of Contents

1. An Introduction to High-Throughput Bioinformatics Data, Keith Baggerly, Kevin Coombes, Jeffrey S. Morris; 2. Hierarchical Mixture Models for Expression Profiles, Michael Newton, Ping Wang, Christina Kendziorski; 3. Bayesian Hierarchical Models for Inference in Microarray Data, Hein, Lewin, Silvia Richardson; 4. Bayesian Process-based Modeling of Two-Channel Microarray Experiments Estimating Absolute mRNA Concentrations, van de Wiel, Holden, Glad, Lyng, Frigessi; 5. Identification of biomarkers in classification and clustering of high-throughput data, Tadesse, Marina Vannucci, Sha, Kim; 6. Modeling nonlinear gene interactions using Bayesian MARS, Baladandayuthapani, Chris C. Holmes, Bani K. Mallick, Raymond J. Carroll; 7. Models for probability of under- and overexpression: The POE scale, Elizabeth Garrett-Mayer, Robert Scharpf; 8. Sparse Statistical Modeling in Gene Expression Genomics, Lucas, Carvalho, Wang, Bild, Nevins, West; 9. Bayesian analysis of cell-cycle gene expression, Zhou, Wakefield, Breeden; 10. Model-based clustering for expression data via Dirichlet process mixture model, Dahl; 11. Interval mapping for Expression Quantitative Trait Loci mapping, Meng Chen, Chris…

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