[NT 42591] Language:
簡体中文
English
繁體中文
[NT 5638] Help
[NT 5480] Login
[NT 59466] Create an account
[NT 5635] Back
[NT 59884] Switch To:
[NT 5556] Labeled
|
[NT 5559] MARC Mode
|
[NT 33762] ISBD
Bayesian modeling in bioinformatics
[NT 42944] Record Type:
[NT 8598] Electronic resources : [NT 40817] monographic
[NT 47261] Author:
DeyDipak,
[NT 47354] Secondary Intellectual Responsibility:
GhoshSamiran,
[NT 47354] Secondary Intellectual Responsibility:
MallickBani K., 1965-
[NT 47351] Place of Publication:
Boca Raton
[NT 47263] Published:
CRC Press;
[NT 47352] Year of Publication:
c2011
[NT 47264] Description:
1 online resource (xxv, 440 p.)ill :
[NT 47298] Series:
Chapman & Hall/CRC biostatistics series
[NT 47266] Subject:
Bayes Theorem -
[NT 47266] Subject:
Computational Biology -
[NT 47266] Subject:
Models, Biological -
[NT 47266] Subject:
Bioinformatics - Statistical methods -
[NT 47266] Subject:
Bayesian statistical decision theory -
[NT 51458] Online resource:
http://www.crcnetbase.com/isbn/978-1-4200-7017-0
[NT 47265] Notes:
"A Chapman & Hall book."
[NT 51398] Summary:
"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
[NT 50961] ISBN:
9781420070187electronic bk.
[NT 50961] ISBN:
1420070185electronic bk.
[NT 60779] Content Note:
List of Tables -- List of Figures -- Preface -- Symbol Description -- Chapter 1: Estimation and Testing in Time-Course Microarray Experiments -- Chapter 2: Classification for Differential Gene Expression Using Bayesian Hierarchical Models -- Chapter 3: Applications of the Mode Oriented Stochastic Search (MOSS) Algorithm for Discrete Multi-Way Data to Genomewide Studies -- Chapter 4: Nonparametric Bayesian Bioinformatics -- Chapter 5: Measurement Error and Survival Model for cDNA Microarrays -- Chapter 6: Bayesian Robust Inference for Differential Gene Expression
Bayesian modeling in bioinformatics
Dey, Dipak
Bayesian modeling in bioinformatics
/ edited by Dipak K. Dey, Samiran Ghosh, Bani K. Mallick - Boca Raton : CRC Press, c2011. - 1 online resource (xxv, 440 p.) ; ill. - (Chapman & Hall/CRC biostatistics series ; 34).
List of Tables -- List of Figures -- Preface -- Symbol Description -- Chapter 1: Estimation and Testing in Time-Course Microarray Experiments -- Chapter 2: Classification for Differential Gene Expression Using Bayesian Hierarchical Models -- Chapter 3: Applications of the Mode Oriented Stochastic Search (MOSS) Algorithm for Discrete Multi-Way Data to Genomewide Studies -- Chapter 4: Nonparametric Bayesian Bioinformatics -- Chapter 5: Measurement Error and Survival Model for cDNA Microarrays -- Chapter 6: Bayesian Robust Inference for Differential Gene Expression.
"A Chapman & Hall book."Description based on print version record.
Includes bibliographical references and index.
ISBN 9781420070187ISBN 1420070185
Bayes TheoremComputational BiologyModels, BiologicalBioinformaticsBayesian statistical decision theory -- Statistical methods
Ghosh, Samiran
Bayesian modeling in bioinformatics
LDR
:02730clm2 2200289 4500
001
290229
009
ocn671643968
010
1
$a
9781420070187
$b
electronic bk.
010
1
$a
1420070185
$b
electronic bk.
100
$a
20140307d2011 k y0engy01 b
101
0
$a
eng
102
$a
us
$b
fl
105
$a
a a 001yy
200
1
$a
Bayesian modeling in bioinformatics
$f
edited by Dipak K. Dey, Samiran Ghosh, Bani K. Mallick
204
1
$a
electronic resource
210
$a
Boca Raton
$d
c2011
$c
CRC Press
215
1
$a
1 online resource (xxv, 440 p.)
$c
ill
225
2
$a
Chapman & Hall/CRC biostatistics series
$v
34
300
$a
"A Chapman & Hall book."
300
$a
Description based on print version record
320
$a
Includes bibliographical references and index
327
1
$a
List of Tables -- List of Figures -- Preface -- Symbol Description -- Chapter 1: Estimation and Testing in Time-Course Microarray Experiments -- Chapter 2: Classification for Differential Gene Expression Using Bayesian Hierarchical Models -- Chapter 3: Applications of the Mode Oriented Stochastic Search (MOSS) Algorithm for Discrete Multi-Way Data to Genomewide Studies -- Chapter 4: Nonparametric Bayesian Bioinformatics -- Chapter 5: Measurement Error and Survival Model for cDNA Microarrays -- Chapter 6: Bayesian Robust Inference for Differential Gene Expression
330
$a
"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
410
0
$1
2001
$a
Chapman & Hall/CRC biostatistics series
$v
34
606
$a
Bayes Theorem
$2
mesh
$3
339998
606
$a
Computational Biology
$2
mesh
$3
340203
606
$a
Models, Biological
$2
lc
$3
325399
606
$a
Bioinformatics
$x
Statistical methods
$2
lc
$3
341459
606
$a
Bayesian statistical decision theory
$2
lc
$3
271778
676
$a
570.285
$v
22
680
$a
QH324.2
$b
D49 2011eb
686
$a
QH 324.2
700
1
$a
Dey
$b
Dipak
$3
341456
702
1
$a
Ghosh
$b
Samiran
$3
341457
702
1
$a
Mallick
$b
Bani K.
$f
1965-
$3
341458
856
4 0
$u
http://www.crcnetbase.com/isbn/978-1-4200-7017-0
[NT 59758] based on 0 [NT 59757] review(s)
[NT 59725] Reviews
[NT 59886] Add a review
[NT 59885] and share your thoughts with other readers
Export
[NT 5501410] pickup library
[NT 42721] Processing
...
[NT 48336] Change password
[NT 5480] Login