Marina Vannucci

Bayesian Inference for Gene Expression and Proteomics

Herausgeber: Do, Kim-Anh; Vannucci, Marina; Müller, Peter

• Gebundenes Buch

Produktbeschreibung

Expert overviews of Bayesian methodology, tools, and software for multi-platform high-throughput experimentation.

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Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 456
• Erscheinungstermin: 24. Juli 2006
• Englisch
• Abmessung: 235mm x 161mm x 29mm
• Gewicht: 739g
• ISBN-13: 9780521860925
• ISBN-10: 052186092X
• Artikelnr.: 21974401

Autorenporträt

Professor Do has significant publications contributing towards her fields of interest including efficient bootstrap methods and empirical likelihood, classification and functional methods with smoothing, Bayesian methods for the genetic analysis of twin and family data, and general biostatistical methods applicable to medical research. One of her current collaborative research focuses with colleagues at M.D. Anderson Cancer Center is towards the development of statistical methods to analyze data produced from high-throughput technologies, such as cDNA microarrays and serial analysis of gene expression (SAGE), to phage peptide libraries and proteomic profiles generated by mass spectrometry, with the goal of revolutionizing the diagnosis, classification, and ultimately the treatment of diseases, including cancer. Specifically, Professor Do has developed a program (GENECLUST) written in Splus/R and C with a JAVA interface that can be implemented on a variety of platforms (Linux, Solaris, DEC, Windows). GENECLUST is basically an efficient implementation of the gene-shaving procedure described by Hastie et al (2000). Another program (BAYESMIX), also written in Splus/R and C, implements a Bayesian mixture model for differential gene expression.
Professor Mueller's research interests and contributions are in the areas of Markov chain Monte Carlo posterior simluation, nonparametric Bayesian inference, hierarchical models, mixture models, and Bayesian decision problems. In recent research, he has developed related models and inference approaches for applications to bioinformatics problems. In particular, the use of Dirichlet process mixture models for inference in microarray group comparison experiments, decision theoretic solutions to sample size choice in high throughput gene and protein expression experiments, and the use of hierarchical mixture of Beta models for inference in mass/charge spectra.
Professor Vannucci's research has focused on the theory and practice of Bayesian variable selection techniques and on the development of wavelt-based statistical models and their applications. Her work has been often motivated by real problems that needed to be addressed with suitable statistical methods. DNA microarray data are characterized by many variables (gene expressions) and relatively few samples. These studies often aim either at predicting different types of tissues or diseases or at the discovery of unknown subtypes (particularly in cancer studies). In addition, it is important that the identified molecular classes are defined on a msall number of genes that can serve as biomarkers for improved diagnosis and therapeutic intervention. Professor Vannucci's work has focused on the development of Bayesian methods that offer a coherent framework in which variable selection and classification or clustering of the samples are performed simultaneously. Her word has been applied to the identification of molecular signatures predictive of different stages of rheumatoids arthritis (RA), to microarray data on endometroid endometrial cancer, and to ovarian cancer prediction based on proteomic data. A recent trend in the bioinformatics field has focused on the integration of data of different forms. Professor Vannucci has worked on models that combine DNA microarray data with genome sequences. The method uses Bayesian variable selection techniques to identify DNA-binding sites for regulatory factors and has been applied to S. cerevisiae and S. pombe genomes using microarray data from environmental stress experiments.

Inhaltsangabe

1. An introduction to high-throughput bioinformatics data Keith Baggerly,
Kevin Coombes and Jeffrey S. Morris; 2. Hierarchical mixture models for
expression profiles Michael Newton, Ping Wang and Christina Kendziorski; 3.
Bayesian hierarchical models for inference in microarray data Anne-Mette K.
Hein, Alex Lewin and Sylvia Richardson; 4. Bayesian process-based modeling
of two-channel microarray experiments estimating absolute mRNA
concentrations 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 Mahlet Tadesse,
Marina Vannucci, Naijun Sha and Sinae Kim; 6. Modeling nonlinear gene
interactions using Bayesian MARS Veerabhadran Baladandayuthapani, Chris C.
Holmes, Bani K. Mallick and Raymond J. Carroll; 7. Models for probability
of under- and over-expression: the POE scale Elizabeth Garrett-Mayer and
Robert Scharpf; 8. Sparse statistical modelling in gene expression genomics
Joseph Lucas, Carlos Carvalho, Quanli Wang, Andrea Bild, Joseph Nevins and
Mike West; 9. Bayesian analysis of cell-cycle gene expression Chuan Zhou,
Jon Wakefield and Linda L. Breeden; 10. Model-based clustering for
expression data via a Dirichlet process mixture model David Dahl; 11.
Interval mapping for Expression Quantitative Trait Loci mapping Meng Chen
and Christina Kendziorski; 12. Bayesian mixture model for gene expression
and protein profiles Michele Guindani, Kim-Anh Do, Peter Müller and Jeffrey
S. Morris; 13. Shrinkage estimation for SAGE data using a mixture Dirichlet
prior Jeffrey S. Morris, Kevin Coombes and Keith Baggerly; 14. Analysis of
mass spectrometry data using Bayesian wavelet-based functional mixed models
Jeffrey S. Morris, Philip J. Brown, Keith Baggerly and Kevin Coombes; 15.
Nonparametric models for proteomic peak identification and quantification
Merlise Clyde, Leanna House and Robert Wolpert; 16. Bayesian modeling and
inference for sequence motif discovery Mayetri Gupta and Jun S. Liu; 17.
Identifying of DNA regulatory motifs and regulators by integrating gene
expression and sequence data Deuk Woo Kwon, Sinae Kim, David Dahl, Michael
Swartz, Mahlet Tadesse and Marina Vannucci; 18. A misclassification model
for inferring transcriptional regulatory networks Ning Sun and Hongyu Zhao;
19. Estimating cellular signaling from transcription data Andrew V.
Kossenkov, Ghislain Bidaut and Michael Ochs; 20. Computational methods for
learning Bayesian networks from high-throughput biological data Bradley
Broom and Devika Subramanian; 21. Modeling transcriptional regulation:
Bayesian networks and informative priors Alexander J. Hartemink; 22. Sample
size choice for microarray experiments Peter Müller, Christian Robert and
Judith Rousseau.