4. Algorithmic Methods for the Analysis of Gene Expression Data

  1. Amiya Nayak B.Math., Ph.D. Adjunct Research Professor Associate Editor Full Professor2 and
  2. Ivan Stojmenović Ph.D. Chair Professor founder editor-in-chief2,3
  1. Hongbo Xie,
  2. Uros Midic,
  3. Slobodan Vucetic and
  4. Zoran Obradovic

Published Online: 1 MAR 2007

DOI: 10.1002/9780470175668.ch4

Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems

Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems

How to Cite

Xie, H., Midic, U., Vucetic, S. and Obradovic, Z. (2007) Algorithmic Methods for the Analysis of Gene Expression Data, in Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems (eds A. Nayak and I. Stojmenović), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470175668.ch4

Editor Information

  1. 2

    SITE, University of Ottawa, 800 King Edward Ave., Ottawa, ON K1N 6N5, Canada

  2. 3

    EECE, University of Birmingham, UK

Author Information

  1. Center for Information Science and Technology, Temple University, 300 Wachman Hall, 1805 N. Broad St., Philadelphia, PA 19122, USA

Publication History

  1. Published Online: 1 MAR 2007
  2. Published Print: 14 FEB 2008

ISBN Information

Print ISBN: 9780470044926

Online ISBN: 9780470175668

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Keywords:

  • gene expression data - algorithmic methods;
  • microarray data preprocessing;
  • metabolic pathways and biological system mechanisms

Summary

The traditional approach to molecular biology consists of studying a small number of genes or proteins that are related to a single biochemical process or pathway. A major paradigm shift recently occurred with the introduction of gene-expression microarrays that measure the expression levels of thousands of genes at once. These comprehensive snapshots of gene activity can be used to investigate metabolic pathways, identify drug targets, and improve disease diagnosis. However, the sheer amount of data obtained using high throughput microarray experiments and the complexity of the existing relevant biological knowledge is beyond the scope of manual analysis. Thus, the bioinformatics algorithms that help analyze such data are a very valuable tool for biomedical science. First, a brief overview of the microarray technology and concepts that are important for understanding the remaining sections are described. Second, microarray data preprocessing, an important topic that has drawn as much attention from the research community as the data analysis itself is discussed. Finally, some of the more important methods for microarray data analysis are described and illustrated with examples and case studies.