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Meta-analysis and Combining Information in Genetics and Genomics
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Meta-analysis and Combining Information in Genetics and Genomics

Book Details

Format Paperback / Softback
ISBN-10 1138116114
ISBN-13 9781138116115
Publisher Taylor & Francis Ltd
Imprint CRC Press
Country of Manufacture GB
Country of Publication GB
Publication Date Jun 14th, 2017
Print length 360 Pages
Weight 670 grams
Ksh 14,200.00
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This book provides many novel techniques for analyzing data from modern biological studies that involve multiple data sets, either of the same type or multiple data sources. It addresses the combination of similar data types: genotype data from genome-wide linkage scans and data derived from microarray gene expression experiments, and solves data combination problems that can arise within the same basic framework. It demonstrates the combined analysis of different data types, showing data combination approaches in action across a wide variety of genome-scale investigations.

Novel Techniques for Analyzing and Combining Data from Modern Biological Studies
Broadens the Traditional Definition of Meta-Analysis

With the diversity of data and meta-data now available, there is increased interest in analyzing multiple studies beyond statistical approaches of formal meta-analysis. Covering an extensive range of quantitative information combination methods, Meta-analysis and Combining Information in Genetics and Genomics looks at how to analyze multiple studies from a broad perspective.

After presenting the basic ideas and tools of meta-analysis, the book addresses the combination of similar data types: genotype data from genome-wide linkage scans and data derived from microarray gene expression experiments. The expert contributors show how some data combination problems can arise even within the same basic framework and offer solutions to these problems. They also discuss the combined analysis of different data types, giving readers an opportunity to see data combination approaches in action across a wide variety of genome-scale investigations.

As heterogeneous data sets become more common, biological understanding will be significantly aided by jointly analyzing such data using fundamentally sound statistical methodology. This book provides many novel techniques for analyzing data from modern biological studies that involve multiple data sets, either of the same type or multiple data sources.


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