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Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis pp 239–256Cite as

Evolutionary Computation for the Interpretation of Metabolomic Data

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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.

The fewer data needed, the better the information. And an overload of information, that is, anything much beyond what is truly needed, leads to information blackout. It does not enrich, but impoverishes.Peter F. Drucker - Management: Tasks, Responsibilities, Practices

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Authors and Affiliations

  1. Institute of Biological Sciences, University of Wales, Aberystwyth, SY23 3DD, UK

    Royston Goodacre

  2. Department of Chemistry, University of Manchester Institute of Science and Technology, PO Box 88, Sackville St., Manchester, M60 1QD, UK

    Royston Goodacre & Douglas B. Kell

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  1. Royston Goodacre
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  2. Douglas B. Kell
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Editors and Affiliations

  1. Pharmacia Corporation, Chesterfield, MO, 63198, USA

    George G. Harrigan

  2. Department of Chemistry, UMIST, PO Box 88, Manchester, M60 1QD, UK

    Royston Goodacre

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Goodacre, R., Kell, D.B. (2003). Evolutionary Computation for the Interpretation of Metabolomic Data. In: Harrigan, G.G., Goodacre, R. (eds) Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0333-0_13

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