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Literature Based Bayesian Analysis Of Gene Expression Data


Literature Based Bayesian Analysis Of Gene Expression Data
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Literature Based Bayesian Analysis Of Gene Expression Data


Literature Based Bayesian Analysis Of Gene Expression Data
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Author : Lijing Xu
language : en
Publisher:
Release Date : 2010

Literature Based Bayesian Analysis Of Gene Expression Data written by Lijing Xu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.




Bayesian Analysis Of Gene Expression Data


Bayesian Analysis Of Gene Expression Data
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Author : Bani K. Mallick
language : en
Publisher: John Wiley & Sons
Release Date : 2009-07-20

Bayesian Analysis Of Gene Expression Data written by Bani K. Mallick and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-20 with Mathematics categories.


The field of high-throughput genetic experimentation is evolving rapidly, with the advent of new technologies and new venues for data mining. Bayesian methods play a role central to the future of data and knowledge integration in the field of Bioinformatics. This book is devoted exclusively to Bayesian methods of analysis for applications to high-throughput gene expression data, exploring the relevant methods that are changing Bioinformatics. Case studies, illustrating Bayesian analyses of public gene expression data, provide the backdrop for students to develop analytical skills, while the more experienced readers will find the review of advanced methods challenging and attainable. This book: Introduces the fundamentals in Bayesian methods of analysis for applications to high-throughput gene expression data. Provides an extensive review of Bayesian analysis and advanced topics for Bioinformatics, including examples that extensively detail the necessary applications. Accompanied by website featuring datasets, exercises and solutions. Bayesian Analysis of Gene Expression Data offers a unique introduction to both Bayesian analysis and gene expression, aimed at graduate students in Statistics, Biomedical Engineers, Computer Scientists, Biostatisticians, Statistical Geneticists, Computational Biologists, applied Mathematicians and Medical consultants working in genomics. Bioinformatics researchers from many fields will find much value in this book.



Bayesian Inference For Gene Expression And Proteomics


Bayesian Inference For Gene Expression And Proteomics
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Author : Kim-Anh Do
language : en
Publisher: Cambridge University Press
Release Date : 2006-07-24

Bayesian Inference For Gene Expression And Proteomics written by Kim-Anh Do and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-07-24 with Mathematics categories.


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



Handbook Of Statistical Genomics


Handbook Of Statistical Genomics
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Author : David J. Balding
language : en
Publisher: John Wiley & Sons
Release Date : 2019-07-09

Handbook Of Statistical Genomics written by David J. Balding and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-09 with Science categories.


A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics.



Statistical Analysis Of Gene Expression Microarray Data


Statistical Analysis Of Gene Expression Microarray Data
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Author : Terry Speed
language : en
Publisher: CRC Press
Release Date : 2003-03-26

Statistical Analysis Of Gene Expression Microarray Data written by Terry Speed and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-03-26 with Mathematics categories.


Although less than a decade old, the field of microarray data analysis is now thriving and growing at a remarkable pace. Biologists, geneticists, and computer scientists as well as statisticians all need an accessible, systematic treatment of the techniques used for analyzing the vast amounts of data generated by large-scale gene expression studies



Bayesian Data Analysis Third Edition


Bayesian Data Analysis Third Edition
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Author : Andrew Gelman
language : en
Publisher: CRC Press
Release Date : 2013-11-01

Bayesian Data Analysis Third Edition written by Andrew Gelman and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-01 with Mathematics categories.


Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.



The Analysis Of Gene Expression Data


The Analysis Of Gene Expression Data
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Author : Giovanni Parmigiani
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-11

The Analysis Of Gene Expression Data written by Giovanni Parmigiani and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-11 with Medical categories.


This book presents practical approaches for the analysis of data from gene expression micro-arrays. It describes the conceptual and methodological underpinning for a statistical tool and its implementation in software. The book includes coverage of various packages that are part of the Bioconductor project and several related R tools. The materials presented cover a range of software tools designed for varied audiences.



A First Course In Bayesian Statistical Methods


A First Course In Bayesian Statistical Methods
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Author : Peter D. Hoff
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-06-02

A First Course In Bayesian Statistical Methods written by Peter D. Hoff and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-02 with Mathematics categories.


A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.



Gene Expression Analysis


Gene Expression Analysis
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Author : Nalini Raghavachari
language : en
Publisher: Springer Nature
Release Date : 2025-02-03

Gene Expression Analysis written by Nalini Raghavachari and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-03 with Science categories.


This second edition volume expands on the previous edition with updates on the latest methodologies in the transcriptomics field. The chapters in this book cover topics such as spatial omics, long-read sequencing technology, tissue microarrays, analysis of saliva and extracellular vesicles, machine learning and artificial intelligence-based approaches for analysis of singe cells transcriptome, and large sets of data on multi-omics including transcriptomics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and practical, Gene Expression Analysis: Methods and Protocols, Second Edition is a valuable resource for advanced undergraduate and graduate students studying gene expression analysis, and scientists interested in learning more about this rapidly advancing field.



Resampling Based Multiple Testing


Resampling Based Multiple Testing
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Author : Peter H. Westfall
language : en
Publisher: John Wiley & Sons
Release Date : 1993-01-12

Resampling Based Multiple Testing written by Peter H. Westfall and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-01-12 with Mathematics categories.


Combines recent developments in resampling technology (including the bootstrap) with new methods for multiple testing that are easy to use, convenient to report and widely applicable. Software from SAS Institute is available to execute many of the methods and programming is straightforward for other applications. Explains how to summarize results using adjusted p-values which do not necessitate cumbersome table look-ups. Demonstrates how to incorporate logical constraints among hypotheses, further improving power.