Analyzing Microarray Gene Expression Data
Will be published approx. on 24. August 2004
Book
Hardback
352 pages
978-0-471-22616-1 (ISBN)
Description
Emphasis on clustering of data of gene tissues
* Includes new research findings and activities in molecular biology
* Highlights the important general field of bioinformatics and genomics and discusses the impact of microarray analysis on both
Reviews / Votes
"...well written, logically organized...useful to those unfamiliar with the microarray literature..." (Journal of the American Statistical Association, December 2005) "....would serve as a very good resource for a graduate topics seminar in advanced applied statistics." (Short Book Reviews, Vol.25, No.1, April 2005)"For the intended audience of biostatisticians and biologists...this book will be an asset." (E-STREAMS, February 2005)
"I liked this book...expands on both the tools and applications of microarray data analysis..." (Technometrics, February 2005)
More details
Product info
gebunden
Series
Edition
1. Auflage
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Illustrations
Charts: 2 B&W, 0 Color; Photos: 15 B&W, 0 Color; Drawings: 2 B&W, 0 Color; Screen captures: 2 B&W, 0 Color; Graphs: 51 B&W, 0 Color
Dimensions
Height: 244 mm
Width: 164 mm
Thickness: 23 mm
Weight
649 gr
ISBN-13
978-0-471-22616-1 (9780471226161)
Schweitzer Classification
Other editions
Additional editions
Geoffrey McLachlan | Kim-Anh Do | Christophe Ambroise
Analyzing Microarray Gene Expression Data
E-Book
02/2005
Wiley
€146.99
Available for download
Persons
GEOFFREY J. McLACHLAN, PhD, is Professor of Statistics at the University of Queensland, Australia, and the author of four very successful statistical texts. KIM-ANH DO, PhD, is Professor of Biostatistics at the University of Texas MD Anderson Cancer Center in Houston, Texas. CHRISTOPHE AMBROISE, PhD, is Lecturer at the Université de Technologie de Compiègne in France.
Content
Preface.
1. Microarrays in Gene Expression Studies.
2. Cleaning and Normalization.
3. Some Cluster Analysis Methods.
4. Clustering of Tissue Samples.
5. Screening and Clustering of Genes.
6. Discriminant Analysis.
7. Supervised Classification of Tissue Samples.
8. Linking Microarray Data with Survival Analysis.
References.
Author Index.
Subject Index.