Evolutionary computation for the interpretation of metabolomic data.

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