Skip to main content
SpringerLink
Log in

Fundamentals of Data Mining in Genomics and Proteomics

  • Book
  • © 2007

Overview

Editors:
  1. Werner Dubitzky

    1. University of Ulster, Coleraine, Northern Ireland

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Martin Granzow

    1. Quantiom Bioinformatics GmbH & Co. KG, Weingarten/Baden, Germany

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Daniel Berrar

    1. University of Ulster, Coleraine, Northern Ireland

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Presents state-of-the-art analytical methods from statistics and data mining for the analysis of high-throughput data from genomics and proteomics

  • Includes supplementary material: sn.pub/extras

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (12 chapters)

  1. Front Matter

    Pages i-xxii
    Download chapter PDF

  2. Introduction to Genomic and Proteomic Data Analysis

    • Daniel Berrar, Martin Granzow, Werner Dubitzky
    Pages 1-37

  3. Design Principles for Microarray Investigations

    • Kathleen F. Kerr
    Pages 39-49

  4. Pre-Processing DNA Microarray Data

    • Benjamin M. Bolstad
    Pages 51-78

  5. Pre-Processing Mass Spectrometry Data

    • Kevin R. Coombes, Keith A. Baggerly, Jeffrey S. Morris
    Pages 79-102

  6. Visualization in Genomics and Proteomics

    • Xiaochun Li, Jaroslaw Harezlak
    Pages 103-122

  7. Clustering — Class Discovery in the Post-Genomic Era

    • Joaquín Dopazo
    Pages 123-148

  8. Feature Selection and Dimensionality Reduction in Genomics and Proteomics

    • Milos Hauskrecht, Richard Pelikan, Michal Valko, James Lyons-Weiler
    Pages 149-172

  9. Resampling Strategies for Model Assessment and Selection

    • Richard Simon
    Pages 173-186

  10. Classification of Genomic and Proteomic Data Using Support Vector Machines

    • Peter Johansson, Markus Ringnér
    Pages 187-202

  11. Networks in Cell Biology

    • Carlos Rodríguez-Caso, Ricard V. Solé
    Pages 203-226

  12. Identifying Important Explanatory Variables for Time-Varying Outcomes

    • Oliver Bembom, Maya L. Petersen, Mark J. van der Laan
    Pages 227-249

  13. Text Mining in Genomics and Proteomics

    • Robert Hoffmann
    Pages 251-274

  14. Back Matter

    Pages 275-281
    Download chapter PDF
Back to top

Keywords

About this book

As natural phenomena are being probed and mapped in ever-greater detail, scientists in genomics and proteomics are facing an exponentially growing vol­ ume of increasingly complex-structured data, information, and knowledge. Ex­ amples include data from microarray gene expression experiments, bead-based and microfluidic technologies, and advanced high-throughput mass spectrom­ etry. A fundamental challenge for life scientists is to explore, analyze, and interpret this information effectively and efficiently. To address this challenge, traditional statistical methods are being complemented by methods from data mining, machine learning and artificial intelligence, visualization techniques, and emerging technologies such as Web services and grid computing. There exists a broad consensus that sophisticated methods and tools from statistics and data mining are required to address the growing data analysis and interpretation needs in the life sciences. However, there is also a great deal of confusion about the arsenal of available techniques and how these should be used to solve concrete analysis problems. Partly this confusion is due to a lack of mutual understanding caused by the different concepts, languages, methodologies, and practices prevailing within the different disciplines.

Editors and Affiliations

  • University of Ulster, Coleraine, Northern Ireland

    Werner Dubitzky, Daniel Berrar

  • Quantiom Bioinformatics GmbH & Co. KG, Weingarten/Baden, Germany

    Martin Granzow

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation