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Analyzing array data using supervised methods

Author:
  • Markus Ringnér
  • Carsten Peterson
  • J Khan
Publishing year: 2002
Language: English
Pages: 403-415
Publication/Series: Pharmacogenomics
Volume: 3
Issue: 3
Document type: Journal article
Publisher: Future Medicine Ltd.

Abstract english

Pharmacogenomics is the application of genomic technologies to drug discovery and development, as well as for the elucidation of the mechanisms of drug action on cells and organisms. DNA microarrays measure genome-wide gene expression patterns and are an important tool for pharmacogenomic applications, such as the identification of molecular targets for drugs, toxicological studies and molecular diagnostics. Genome-wide investigations generate vast amounts of data and there is a need for computational methods to manage and analyze this information. Recently, several supervised methods, in which other information is utilized together with gene expression data, have been used to characterize genes and samples. The choice of analysis methods will influence the results and their interpretation, therefore it is important to be familiar with each method, its scope and limitations. Here, methods with special reference to applications for pharmacogenomics are reviewed.

Keywords

  • Biophysics
  • machine learning
  • genes
  • drug targets
  • DNA chip
  • diagnostic prediction
  • diagnostic classification
  • artificial neural networks
  • bioinformatics
  • microarray
  • support vector machines
  • target identification

Other

Published
  • ISSN: 1462-2416
Markus Ringnér
E-mail: markus [dot] ringner [at] biol [dot] lu [dot] se

Research engineer

Molecular Cell Biology

B-A317

Sölvegatan 35, Lund

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