Methods in Microarray Normalization

Name: Methods in Microarray Normalization
Brand: Taylor & Francis Inc
Price: 193.75 AUD
Availability: BackOrder

By: Phillip Stafford (Editor)

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Hardcover | 31 January 2008 | Edition Number 1

At a Glance

Hardcover
320 Pages

Dimensions(cm)
23.4 x 15.6

Hardcover

RRP $221.00

$193.75

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Scientists can use molecular profiling microarrays to compare healthy cells with their diseased counterparts and develop gene-specific treatments. Finding the best way to interpret original profiling data into accurate trends, however, continues to drive the development of normalization algorithms and software tools.

Methods in Microarray Normalization compiles the most useful and novel techniques for the first time into a single, organized source. Experts in the field provide a diverse view of the mathematical processes that are important in normalizing data and avoiding inherent systematic biases. They also review useful software, including discussions on key algorithms, comparative data, and download locations.

The book discusses the use of early normalization techniques for new profiling methods and includes strategies for assessing the utility of various normalization algorithms. It presents the latest microarray innovations from companies such as Agilent, Affymetrix, and GeneGo as well as new normalization methods for protein and CGH arrays, many of which are applicable for antibody, microRNA, methylation, and siRNA arrays.

Methods in Microarray Normalization provides scientists with a complete resource on the most effective tools available for maximizing microarray data in biochemical research.

Daniel E. Levy, editor of the Drug Discovery Series, is the founder of DEL BioPharma, a consulting service for drug discovery programs. He also maintains a blog that explores organic chemistry.

Industry Reviews

"Methods in Microarray Normalization compiles the most useful and novel techniques for the first time into a single, organized source. Experts in the field provide a diverse view of the mathematical processes that are important in normalizing data and avoiding inherent systematic biases."

- In Anticancer Research, July/August 2008, Vol. 28, No. 4A

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