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Microarray Bioinformatics

Microarray Bioinformatics

Microarray Bioinformatics

Dov Stekel, University of Birmingham
September 2003
Paperback
9780521525879
$46.99
USD
Paperback
USD
eBook

    DNA microarrays have revolutionized molecular biology and are becoming a standard tool in the field. Dov Stekel's book is a comprehensive guide to the mathematics, statistics, and computing required to use microarrays successfully. Unlike traditional molecular biology, the successful use of DNA microarrays requires the application of statistics and computing to design the arrays and experiments, and to analyze and manage the data. This book is written for researchers, clinicians, and laboratory managers.

    • A balanced and comprehensive guide to all microarray bioinformatics, accessible to students from both biology and bioinformatics backgrounds
    • Based on the highly successful microarray bioinformatics course at Oxford University and written by the course's director, an experienced instructor
    • Detailed glossary of terms provided by the Microarray Gene Expression Data Society, who define the standard terms used in the field internationally

    Reviews & endorsements

    "...excellent and clearly written...concise and most informative, a pleasure to read. It should be examined by anyone interested in this means of analysis."
    ASM News

    "The book would be ideal for biologists who wish to gain a grasp of the different analysis techniques available to the microarray user."
    Society for General Microbiology

    See more reviews

    Product details

    September 2003
    Paperback
    9780521525879
    280 pages
    248 × 178 × 17 mm
    0.612kg
    116 b/w illus. 8 colour illus. 58 tables
    Available

    Table of Contents

    • 1. Microarrays: making them and using them
    • 2. Sequence databases for microarrays
    • 3. Computer design of oligonucleotide probes
    • 4. Image processing
    • 5. Normalization
    • 6. Measuring and quantifying microarray variability
    • 7. Analysis of differentially expressed genes
    • 8. Analysis of relationships between genes, tissues or treatments
    • 9. Classification of tissues and samples
    • 10. Experimental design
    • 11. Data storage, standards and sharing
    • Appendix A. Glossary.