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Methods of Microarray Data Analysis II
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Methods of Microarray Data Analysis II : Papers from CAMDA ’01

Softcover reprint of the original 1st ed. 2002

Book Details

Format Paperback / Softback
ISBN-10 1475788312
ISBN-13 9781475788310
Edition Softcover reprint of the original 1st ed. 2002
Publisher Springer-Verlag New York Inc.
Imprint Springer-Verlag New York Inc.
Country of Manufacture US
Country of Publication GB
Publication Date Apr 14th, 2013
Print length 214 Pages
Ksh 15,850.00
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In a single reference, readers can learn about the most up-to-date methods, ranging from data normalization, feature selection, and discriminative analysis to machine learning techniques. Methods of Microarray Data Analysis II focuses on a single data set, using a different method of analysis in each chapter.

Microarray technology is a major experimental tool for functional genomic explorations, and will continue to be a major tool throughout this decade and beyond. The recent explosion of this technology threatens to overwhelm the scientific community with massive quantities of data. Because microarray data analysis is an emerging field, very few analytical models currently exist. Methods of Microarray Data Analysis II is the second book in this pioneering series dedicated to this exciting new field. In a single reference, readers can learn about the most up-to-date methods, ranging from data normalization, feature selection, and discriminative analysis to machine learning techniques.

Currently, there are no standard procedures for the design and analysis of microarray experiments. Methods of Microarray Data Analysis II focuses on a single data set, using a different method of analysis in each chapter. Real examples expose the strengths and weaknesses of each method for a given situation, aimed at helping readers choose appropriate protocols and utilize them for their own data set. In addition, web links are provided to the programs and tools discussed in several chapters. This book is an excellent reference not only for academic and industrial researchers, but also for core bioinformatics/genomics courses in undergraduate and graduate programs.


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