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Bayesian Inference for Gene Expression and Proteomics [Pehme köide]

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Edited by (University of Texas, MD Anderson Cancer Center), Edited by (Rice University, Houston), Edited by (Swiss Federal Institute of Technology, Zürich)
  • Formaat: Paperback / softback, 456 pages, kõrgus x laius x paksus: 221x141x25 mm, kaal: 570 g, 22 Tables, unspecified
  • Ilmumisaeg: 30-Apr-2012
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1107636981
  • ISBN-13: 9781107636989
Teised raamatud teemal:
Bayesian Inference for Gene Expression and ProteomicsSuurem pilt
  • Formaat: Paperback / softback, 456 pages, kõrgus x laius x paksus: 221x141x25 mm, kaal: 570 g, 22 Tables, unspecified
  • Ilmumisaeg: 30-Apr-2012
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1107636981
  • ISBN-13: 9781107636989
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781107636989.html
Märksõnad:
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.

Arvustused

'A text that has a systematic account of Bayesian analysis in computational biology has been needed for a long time. This book is a timely publication entirely devoted to cutting-edge Bayesian methods in genomics and proteomics research and many of its contributors are leading authorities in the field. It is thus an indispensable reference for researchers who are interested in applying Bayesian techniques in their own biological research.' Ping Ma, University of Illinois, Urbana-Champaign ' an authoritative volume presents the state of the art statistical techniques that are starting to make an impact at the forefronts of modern scientific discovery.' Journal of the RSS 'A collection of 22 high quality chapters, authored by several distinguished groups of academic researchers researchers and students will appreciate an authoritative volume like the present.' Z. Q. John Lu, National Institute of Standards and Technology, Gaithersburg 'The editors have done a great job keeping the writing of diverse authors readable without great redundancy This book should be required reading for all graduate students of statistics, statistical researchers in this field, and students and researchers in other fields that use these technologies.' Tapabrata Maita, Journal of the American Statistician 'Overall, I find this text an excellent contribution to the literature on statistical methods for high throughput genomic and proteomic data analysis. The chapters are well written, the case studies are informative, and the range of topics covered is quite broad and generally logically grouped. I would highly recommend this text to both those people already working in the area and those wanting to break in. It is not only suitable for researchers developing their own methodologies but also for applied quantitative scientists looking for the most cutting-edge tools to analyze their high throughput datasets.' J. Sunil Rao, Biometrics

Muu info

Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation.
List of Contributors
xi
Preface xv
1 An Introduction to High-Throughput Bioinformatics Data
1(39)
Keith A. Baggerly
Kevin R. Coombes
Jeffrey S. Morris
1.1 Introduction
1(1)
1.2 Microarrays
2(17)
1.3 SAGE
19(5)
1.4 Mass Spectrometry
24(10)
1.5 Finding Data
34(6)
2 Hierarchical Mixture Models for Expression Profiles
40(13)
Michael A. Newton
Ping Wang
Christina Kendziorski
2.1 Introduction
40(3)
2.2 Dual Character of Posterior Probabilities
43(2)
2.3 Differential Expression as Independence
45(2)
2.4 The Multigroup Mixture Model
47(2)
2.5 Improving Flexibility
49(4)
3 Bayesian Hierarchical Models for Inference in Microarray Data
53(22)
Anne-Mette K. Hein
Alex Lewin
Sylvia Richardson
3.1 Introduction
53(3)
3.2 Bayesian Hierarchical Modeling of Probe Level GeneChip Data
56(11)
3.3 Bayesian Hierarchical Model for Normalization and Differential Expression
67(3)
3.4 Predictive Model Checking
70(5)
4 Bayesian Process-Based Modeling of Two-Channel Microarray Experiments: Estimating Absolute mRNA Concentrations
75(22)
Mark A. van de Wiel
Marit Holden
Ingrid K. Glad
Heidi Lyng
Arnoldo Frigessi
4.1 Introduction
75(3)
4.2 The Hierarchical Model
78(4)
4.3 Reparameterization and Identifiability
82(2)
4.4 MCMC-Based Inference
84(1)
4.5 Validation
85(1)
4.6 Illustration
85(6)
4.7 TransCount Web Site and Computing Times
91(1)
4.8 A Statistical Discussion of the Model
91(2)
4.9 Discussion
93(4)
5 Identification of Biomarkers in Classification and Clustering of High-Throughput Data
97(19)
Mahlet G. Tadesse
Naijun Sha
Sinae Kim
Marina Vannucci
5.1 Introduction
97(3)
5.2 Bayesian Variable Selection in Linear Models
100(1)
5.3 Bayesian Variable Selection in Classification
101(2)
5.4 Bayesian Variable Selection in Clustering via Finite Mixture Models
103(3)
5.5 Bayesian Variable Selection in Clustering via Dirichlet Process Mixture Models
106(2)
5.6 Example: Leukemia Gene Expression Data
108(5)
5.7 Conclusion
113(3)
6 Modeling Nonlinear Gene Interactions Using Bayesian MARS
116(21)
Veerabhadran Baladandayuthapani
Chris C. Holmes
Bani K. Mallick
Raymond J. Carroll
6.1 Introduction
116(2)
6.2 Bayesian MARS Model for Gene Interaction
118(3)
6.3 Computation
121(1)
6.4 Prediction and Model Choice
122(1)
6.5 Examples
123(8)
6.6 Discussion and Summary
131(6)
7 Models for Probability of Under- and Overexpression: The POE Scale
137(18)
Elizabeth Garrett-Mayer
Robert Scharpf
7.1 POE: A Latent Variable Mixture Model
137(1)
7.2 The POE Model
138(6)
7.3 Unsupervised versus Semisupervised POE
144(1)
7.4 Using POE Scale
145(3)
7.5 Example: POE as Applied to Lung Cancer Microarray Data
148(4)
7.6 Discussion
152(3)
8 Sparse Statistical Modelling in Gene Expression Genomics
155(22)
Joseph Lucas
Carlos Carvalho
Quanli Wang
Andrea Bild
Joseph R. Nevins
Mike West
8.1 Perspective
156(1)
8.2 Sparse Regression Modelling
157(5)
8.3 Sparse Regression for Artifact Correction with Affymetrix Expression Arrays
162(5)
8.4 Sparse Latent Factor Models and Latent Factor Regressions
167(6)
8.5 Concluding Comments
173(4)
9 Bayesian Analysis of Cell Cycle Gene Expression Data
177(24)
Chuan Zhou
Jon C. Wakefield
Linda L. Breeden
9.1 Introduction
177(1)
9.2 Previous Studies
178(2)
9.3 Data
180(2)
9.4 Bayesian Analysis of Cell Cycle Data
182(15)
9.5 Discussion
197(4)
10 Model-Based Clustering for Expression Data via a Dirichlet Process Mixture Model
201(18)
David B. Dahl
10.1 Introduction
201(2)
10.2 Model
203(5)
10.3 Inference
208(1)
10.4 Simulation Study
209(3)
10.5 Example
212(4)
10.6 Conclusion
216(3)
11 Interval Mapping for Expression Quantitative Trait Loci
219(19)
Meng Chen
Christina Kendziorski
11.1 Introduction
219(2)
11.2 eQTL Mapping Experiments
221(1)
11.3 QTL Mapping Methods
222(1)
11.4 Currently Available eQTL Mapping Methods
223(2)
11.5 MOM Interval Mapping
225(6)
11.6 Discussion
231(7)
12 Bayesian Mixture Models for Gene Expression and Protein Profiles
238(16)
Michele Guindani
Kim-Anh Do
Peter Muller
Jeffrey S. Morris
12.1 Introduction
238(2)
12.2 A Nonparametric Bayesian Model for Differential Gene Expression
240(3)
12.3 A Mixture of Beta Model for MALDI-TOF Data
243(4)
12.4 A Semiparametric Mixture Model for SAGE Data
247(3)
12.5 Summary
250(4)
13 Shrinkage Estimation for SAGE Data Using a Mixture Dirichlet Prior
254(15)
Jeffrey S. Morris
Keith A. Baggerly
Kevin R. Coombes
13.1 Introduction
254(1)
13.2 Overview of SAGE
255(2)
13.3 Methods for Estimating Relative Abundances
257(3)
13.4 Mixture Dirichlet Distribution
260(3)
13.5 Implementation Details
263(1)
13.6 Simulation Study
264(3)
13.7 Conclusion
267(2)
14 Analysis of Mass Spectrometry Data Using Bayesian Wavelet-Based Functional Mixed Models
269(24)
Jeffrey S. Morris
Philip J. Brown
Keith A. Baggerly
Kevin R. Coombes
14.1 Introduction
270(1)
14.2 Overview of MALDI-TOF
270(4)
14.3 Functional Mixed Models
274(2)
14.4 Wavelet-Based Functional Mixed Models
276(4)
14.5 Analyzing Mass Spectrometry Data Using Wavelet-Based Functional Mixed Models
280(8)
14.6 Conclusion
288(5)
15 Nonparametric Models for Proteomic Peak Identification and Quantification
293(16)
Merlise A. Clyde
Leanna L. House
Robert L. Wolpert
15.1 Introduction
293(1)
15.2 Kernel Models for Spectra
294(2)
15.3 Prior Distributions
296(5)
15.4 Likelihood
301(1)
15.5 Posterior Inference
302(1)
15.6 Illustration
303(2)
15.7 Summary
305(4)
16 Bayesian Modeling and Inference for Sequence Motif Discovery
309(24)
Mayetri Gupta
Jun S. Liu
16.1 Introduction
309(2)
16.2 Biology of Transcription Regulation
311(1)
16.3 Problem Formulation, Background, and General Strategies
312(4)
16.4 A Bayesian Approach to Motif Discovery
316(4)
16.5 Extensions of the Product-Multinomial Motif Model
320(1)
16.6 HMM-Type Models for Regulatory Modules
321(6)
16.7 Model Selection through a Bayesian Approach
327(2)
16.8 Discussion: Motif Discovery Beyond Sequence Analysis
329(4)
17 Identification of DNA Regulatory Motifs and Regulators by Integrating Gene Expression and Sequence Data
333(14)
Deukwoo Kwon
Sinae Kim
David B. Dahl
Michael Swartz
Mahlet G. Tadesse
Marina Vannucci
17.1 Introduction
333(2)
17.2 Integrating Gene Expression and Sequence Data
335(2)
17.3 A Model for the Identification of Regulatory Motifs
337(3)
17.4 Identification of Regulatory Motifs and Regulators
340(4)
17.5 Conclusion
344(3)
18 A Misclassification Model for Inferring Transcriptional Regulatory Networks
347(19)
Ning Sun
Hongyu Zhao
18.1 Introduction
347(1)
18.2 Methods
348(7)
18.3 Simulation Results
355(5)
18.4 Application to Yeast Cell Cycle Data
360(1)
18.5 Discussion
361(5)
19 Estimating Cellular Signaling from Transcription Data
366(19)
Andrew V. Kossenkov
Ghislain Bidaut
Michael F. Ochs
19.1 Introduction
366(4)
19.2 Bayesian Decomposition
370(3)
19.3 Key Biological Databases
373(3)
19.4 Example: Signaling Activity in Saccharomyces cerevisiae
376(4)
19.5 Conclusion
380(5)
20 Computational Methods for Learning Bayesian Networks from High-Throughput Biological Data
385(16)
Bradley M. Broom
Devika Subramanian
20.1 Introduction
385(2)
20.2 Bayesian Networks
387(2)
20.3 Learning Bayesian Networks
389(2)
20.4 Algorithms for Learning Bayesian Networks
391(4)
20.5 Example: Learning Robust Features from Data
395(3)
20.6 Conclusion
398(3)
21 Bayesian Networks and Informative Priors: Transcriptional Regulatory Network Models
401(24)
Alexander J. Hartemink
21.1 Introduction
401(2)
21.2 Bayesian Networks and Bayesian Network Inference
403(4)
21.3 Adding Informative Structure Priors
407(2)
21.4 Applications of Informative Structure Priors
409(9)
21.5 Adding Informative Parameter Priors
418(1)
21.6 Discussion
419(2)
21.7 Availability of Papers and Banjo Software
421(1)
21.8 Acknowledgments
421(4)
22 Sample Size Choice for Microarray Experiments
425
Peter Muller
Christian Robert
Judith Rousseau
22.1 Introduction
425(3)
22.2 Optimal Sample Size as a Decision Problem
428(3)
22.3 Monte Carlo Evaluation of Predictive Power
431(1)
22.4 The Probability Model
432(3)
22.5 Pilot Data
435(1)
22.6 Example
435(1)
22.7 Conclusion
436
Kim-Anh Do is a Professor in the Department of Biostatistics and Applied Mathematics at the University of Texas M. D. Anderson Cancer Center. Her research interests are in computer-intensive statistical methods with recent focus in the development of methodology and software to analyze data produced from high-throughput optimization. Peter Müller is a Professor in the Department of Biostatistics and Applied Mathematics at the University of Texas M. D. Anderson Cancer Center. His research interests and contributions are in the areas of Markov chain Monte Carlo posterior simulation, nonparametric Bayesian inference, hierarchical models, mixture models and Bayesian decisions problems. Marina Vannucci is a Professor of Statistics at Rice University. Her research focuses on the theory and practice of Bayesian variable selection techniques and on the development of wavelet-based statistical models and their applications. Her work is often motivated by real problems that need to be addressed with suitable statistical methods.