Discrimination of Modes of Action of Antifungal Substances by Use of Metabolic Footprinting

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@Article{Allen:2004:AEM,

• author = "Jess Allen and Hazel M. Davey and David Broadhurst and Jem J. Rowland and Stephen G. Oliver and Douglas B. Kell",
• title = "Discrimination of Modes of Action of Antifungal Substances by Use of Metabolic Footprinting",
• journal = "Applied and Environmental Microbiology",
• year = "2004",
• volume = "70",
• number = "10",
• pages = "6157--6165",
• month = oct,
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1128/AEM.70.10.6157-6165.2004",
• abstract = "Diploid cells of Saccharomyces cerevisiae were grown under controlled conditions with a Bioscreen instrument, which permitted the essentially continuous registration of their growth via optical density measurements. Some cultures were exposed to concentrations of a number of antifungal substances with different targets or modes of action (sterol biosynthesis, respiratory chain, amino acid synthesis, and the uncoupler). Culture supernatants were taken and analyzed for their metabolic footprints by using direct-injection mass spectrometry. Discriminant function analysis and hierarchical cluster analysis allowed these antifungal compounds to be distinguished and classified according to their modes of action. Genetic programming, a rule-evolving machine learning strategy, allowed respiratory inhibitors to be discriminated from others by using just two masses. Metabolic footprinting thus represents a rapid, convenient, and information-rich method for classifying the modes of action of antifungal substances.",
• notes = "PMID:",

}

Genetic Programming entries for Jess Allen Hazel M Davey David I Broadhurst Jem J Rowland Stephen G Oliver Douglas B Kell

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