Fundamentals of Data Mining in Genomics and Proteomics
This book presents state-of-the-art analytical methods from statistics and data mining for the analysis of high-throughput data from genomics and proteomics. It adopts an approach focusing on concepts and applications and presents key analytical techniques for the analysis of genomics and proteomics data by detailing their underlying principles, merits and limitations.

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Fundamentals of Data Mining in Genomics and Proteomics
This book presents state-of-the-art analytical methods from statistics and data mining for the analysis of high-throughput data from genomics and proteomics. It adopts an approach focusing on concepts and applications and presents key analytical techniques for the analysis of genomics and proteomics data by detailing their underlying principles, merits and limitations.

109.99 In Stock
Fundamentals of Data Mining in Genomics and Proteomics

Fundamentals of Data Mining in Genomics and Proteomics

Fundamentals of Data Mining in Genomics and Proteomics

Fundamentals of Data Mining in Genomics and Proteomics

Paperback(Softcover reprint of hardcover 1st ed. 2007)

$109.99 
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Overview

This book presents state-of-the-art analytical methods from statistics and data mining for the analysis of high-throughput data from genomics and proteomics. It adopts an approach focusing on concepts and applications and presents key analytical techniques for the analysis of genomics and proteomics data by detailing their underlying principles, merits and limitations.


Product Details

ISBN-13: 9781441942913
Publisher: Springer US
Publication date: 11/04/2010
Edition description: Softcover reprint of hardcover 1st ed. 2007
Pages: 281
Product dimensions: 6.10(w) x 9.25(h) x 0.24(d)

Table of Contents

to Genomic and Proteomic Data Analysis.- Design Principles for Microarray Investigations.- Pre-Processing DNA Microarray Data.- Pre-Processing Mass Spectrometry Data.- Visualization in Genomics and Proteomics.- Clustering — Class Discovery in the Post-Genomic Era.- Feature Selection and Dimensionality Reduction in Genomics and Proteomics.- Resampling Strategies for Model Assessment and Selection.- Classification of Genomic and Proteomic Data Using Support Vector Machines.- Networks in Cell Biology.- Identifying Important Explanatory Variables for Time-Varying Outcomes.- Text Mining in Genomics and Proteomics.
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