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Meta-Analysis and Combining Information in Genetics and Genomics
Contributor(s): Guerra, Rudy (Author), Goldstein, Darlene R. (Author)

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ISBN: 158488522X     ISBN-13: 9781584885221
Publisher: CRC Press
OUR PRICE: $237.50  

Binding Type: Hardcover
Published: July 2009
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Annotation: With contributions from leading experts, Meta-analysis and Combining Information in Genetics and Genomics provides theory, methods, and guiding principles for meta-analysis and combining information for various types of genetic studies. The book begins with an introduction to meta-analysis and combining information as well as statistical genetics, bioinformatics, and proteomics. Addressing the complications inherent in comparing and replicating genetic studies, the contributors carefully examine microarrays, gene mapping, and proteomics. They detail the various techniques used to analyze and combine data from different types of studies. This is the first book devoted to meta-analysis in genetics.

Click for more in this series: Chapman & Hall/CRC Mathematical and Computational Biology
Additional Information
BISAC Categories:
- Mathematics | Probability & Statistics - General
- Science | Life Sciences - Evolution
- Science | Biotechnology
Dewey: 576.501
LCCN: 2009018853
Series: Chapman & Hall/CRC Mathematical and Computational Biology
Physical Information: 1" H x 6.1" W x 9.3" L (1.40 lbs) 360 pages
Features: Bibliography, Illustrated, Index
Review Citations: Scitech Book News 12/01/2009 pg. 74
 
Descriptions, Reviews, Etc.
Publisher Description:

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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