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Evolutionary Computation for the Interpretation of Metabolomic Data

  • Royston Goodacre
  • Douglas B. Kell

Abstract

Post-genomic science is producing bounteous data floods, and as the above quotation indicates the extraction of the most meaningful parts of these data is key to the generation of useful new knowledge. Atypical metabolic fingerprint or metabolomics experiment is expected to generate thousands of data points (samples times variables) of which only a handful might be needed to describe the problem adequately. Evolutionary algorithms are ideal strategies for mining such data to generate useful relationships, rules and predictions. This chapter describes these techniques and highlights their exploitation in metabolomics.

Keywords

Partial Little Square Evolutionary Computation Anal Chim Inductive Logic Programming Rule Induction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Royston Goodacre
    • 1
    • 2
  • Douglas B. Kell
    • 2
  1. 1.Institute of Biological SciencesUniversity of WalesAberystwythUK
  2. 2.Department of ChemistryUniversity of Manchester Institute of Science and TechnologyManchesterUK

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