Explanatory Optimization of Protein Mass Spectrometry via Genetic Search

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@Article{vaidyanathan:2003:AC,
  author =       "Seetharaman Vaidyanathan and David I. Broadhurst and 
                 Douglas B. Kell and Royston Goodacre",
  title =        "Explanatory Optimization of Protein Mass Spectrometry
                 via Genetic Search",
  journal =      "Analytical Chemistry",
  year =         "2003",
  volume =       "75",
  number =       "23",
  pages =        "6679--6686",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://dbkgroup.org/Papers/AnalChem75(6679-6686).pdf",
  DOI =          "doi:10.1021/ac034669a",
  size =         "8 pages",
  abstract =     "Optimizing experimental conditions for the effective
                 analysis of intact proteins by mass spectrometry is
                 challenging, as many analytical factors influence the
                 spectral quality, often in very different ways for
                 different proteins and especially with complex protein
                 mixtures. We show that genetic search methods are
                 highly effective in this kind of optimization and that
                 it was possible in 6 generations with a total of <500
                 experiments out of some 1014 to find good combinations
                 of experimental variables (electrospray ionization mass
                 spectral settings) that would not have been detected by
                 optimizing each variable alone (i.e., the search space
                 is epistatic). Moreover, by inspecting the evolution of
                 the variables to be optimized using genetic
                 programming, we discovered an important relationship
                 between two of the mass spectrometer settings that
                 accounts for much of this success. Specifically, the
                 conditions that were evolved included very low values
                 of skimmer 1 voltage (the sample cone) and a skimmer 2
                 voltage (extraction cone) above a threshold that would
                 nevertheless minimize the potential difference between
                 the sample and extraction skimmers. The discovery of
                 this relationship demonstrates the
                 hypothesis-generating ability of genetic search in
                 optimization processes where the size of the search
                 space means that little or no a priori knowledge of the
                 optimal conditions is available.",
}

Genetic Programming entries for Seetharaman Vaidyanathan David I Broadhurst Douglas B Kell Royston Goodacre

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