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Bioinformatics and biomarker discovery : "omic" data analysis for personalized medicine

Author: Francisco Azuaje
Publisher: Chichester, UK ; Hoboken, NJ : Wiley-Blackwell, 2010.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This book is designed to introduce biologists, clinicians and computational researchers to fundamental data analysis principles, techniques and tools for supporting the discovery of biomarkers and the implementation of diagnostic/prognostic systems. The focus of the book is on how fundamental statistical and data mining approaches can support biomarker discovery and evaluation, emphasising applications based on  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Azuaje, Francisco.
Bioinformatics and biomarker discovery.
Chichester, UK ; Hoboken, NJ : Wiley-Blackwell, 2010
(DLC) 2009027776
(OCoLC)423572691
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Francisco Azuaje
ISBN: 9780470686430 047068643X
OCLC Number: 592756310
Description: 1 online resource (xviii, 230 pages) : illustrations
Contents: Bioinformatics and Biomarker Discovery: ''Omic'' Data Analysis for Personalized Medicine; Contents; Author and guest contributor biographies; Acknowledgements; Preface; 1 Biomarkers and bioinformatics; 2 Review of fundamental statistical concepts; 3 Biomarker-based prediction models: design and interpretation principles; 4 An introduction to the discovery and analysis of genotype-phenotype associations; Guest commentary on chapter 4: Integrative approaches to genotype-phenotype association discovery; 5 Biomarkers and gene expression data analysis.
Responsibility: Francisco Azuaje.

Abstract:

This book is designed to introduce biologists, clinicians and computational researchers to fundamental data analysis principles, techniques and tools for supporting the discovery of biomarkers and the implementation of diagnostic/prognostic systems. The focus of the book is on how fundamental statistical and data mining approaches can support biomarker discovery and evaluation, emphasising applications based on different types of "omic" data. The book also discusses design factors, requirements and techniques for disease screening, diagnostic and prognostic applications. Readers are provided w.

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