Book Description
Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation.
Author : Kim-Anh Do
Publisher : Cambridge University Press
Page : 437 pages
File Size : 40,73 MB
Release : 2006-07-24
Category : Mathematics
ISBN : 052186092X
Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation.
Author : Bani K. Mallick
Publisher : John Wiley & Sons
Page : 252 pages
File Size : 30,82 MB
Release : 2009-07-20
Category : Mathematics
ISBN : 9780470742815
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.
Author : Dabao Zhang
Publisher :
Page : 194 pages
File Size : 32,87 MB
Release : 2003
Category :
ISBN :
Author : Dipak K. Dey
Publisher : CRC Press
Page : 466 pages
File Size : 41,87 MB
Release : 2010-09-03
Category : Mathematics
ISBN : 1420070185
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 c
Author : Do Kim anh
Publisher :
Page : 437 pages
File Size : 42,77 MB
Release : 2006
Category :
ISBN :
Author : Niansheng Tang
Publisher : BoD – Books on Demand
Page : 120 pages
File Size : 32,22 MB
Release : 2020-07-15
Category : Mathematics
ISBN : 1838803858
Due to great applications in various fields, such as social science, biomedicine, genomics, and signal processing, and the improvement of computing ability, Bayesian inference has made substantial developments for analyzing complicated data. This book introduces key ideas of Bayesian sampling methods, Bayesian estimation, and selection of the prior. It is structured around topics on the impact of the choice of the prior on Bayesian statistics, some advances on Bayesian sampling methods, and Bayesian inference for complicated data including breast cancer data, cloud-based healthcare data, gene network data, and longitudinal data. This volume is designed for statisticians, engineers, doctors, and machine learning researchers.
Author : Francisco Azuaje
Publisher : John Wiley & Sons
Page : 284 pages
File Size : 33,51 MB
Release : 2005-06-24
Category : Science
ISBN : 0470094400
Data Analysis and Visualization in Genomics and Proteomics is the first book addressing integrative data analysis and visualization in this field. It addresses important techniques for the interpretation of data originating from multiple sources, encoded in different formats or protocols, and processed by multiple systems. One of the first systematic overviews of the problem of biological data integration using computational approaches This book provides scientists and students with the basis for the development and application of integrative computational methods to analyse biological data on a systemic scale Places emphasis on the processing of multiple data and knowledge resources, and the combination of different models and systems
Author : Riten Mitra
Publisher : Springer
Page : 448 pages
File Size : 41,23 MB
Release : 2015-07-25
Category : Medical
ISBN : 3319195182
As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters cover: clinical trials, spatial inference, proteomics, genomics, clustering, survival analysis and ROC curve.
Author : Lijing Xu
Publisher :
Page : 202 pages
File Size : 43,1 MB
Release : 2010
Category :
ISBN :
Author : Yu Liu
Publisher : CRC Press
Page : 406 pages
File Size : 22,27 MB
Release : 2014-02-24
Category : Mathematics
ISBN : 1482246627
This title includes a number of Open Access chapters.The book introduces bioinformatic and statistical methodology and shows approaches to bias correction and error estimation. It also presents quantitative methods for genome and proteome analysis.