The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Analyzing array data using supervised methods

På svenska

Author

Summary, in 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.

Publishing year

2002-05

Language

English

Pages

403-415

Publication/Series

Pharmacogenomics

Volume

3

Issue

3

Document type

Journal article review

Publisher

Future Medicine Ltd.

Topic

  • Bioinformatics and Systems Biology

Keywords

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

Status

Published

ISBN/ISSN/Other

  • ISSN: 1462-2416