Searching for a single mathematical function to address the nonlinear retention time shifts problem in nanoLC-MS data: A fuzzy-evolutionary computational proteomics approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Barton:2010:ieeeCIBCB,
  author =       "Alan J. Barton",
  title =        "Searching for a single mathematical function to
                 address the nonlinear retention time shifts problem in
                 nanoLC-MS data: A fuzzy-evolutionary computational
                 proteomics approach",
  booktitle =    "2010 IEEE Symposium on Computational Intelligence in
                 Bioinformatics and Computational Biology (CIBCB)",
  year =         "2010",
  month =        may,
  abstract =     "Proteomics involves collecting and analysing
                 information about proteins within one or more complex
                 samples in order to address a biological problem. One
                 methodology is the use of high performance liquid
                 chromatography coupled mass spectrometry (nanoLC-MS).
                 In such a case, the accurate determination of
                 non-linear peptide retention times between runs is
                 expected to increase the number of identified peptides
                 and hence, proteins. There are many approaches when
                 using a computer for such a problem; including very
                 interactive to completely non-interactive algorithms
                 for finding global and local functions that may be
                 either explicit or implicit. This paper extends
                 previous work and explores finding an explicit global
                 function for which two stages are involved: i)
                 computation of a set of candidate functions (results)
                 by the algorithm, and ii) searching within the set for
                 patterns of interest. For the first stage, three
                 classes of approximating global functions are
                 considered: Class 1 functions that have a completely
                 unknown structure, Class 2 functions that have a tiny
                 amount of domain knowledge incorporated, and Class 3
                 functions that have a small amount of domain knowledge
                 incorporated. For the second stage, some issues with
                 current similarity measures for mathematical
                 expressions are discussed and a new measure is
                 proposed. Preliminary experimental results with an
                 Evolutionary Computation algorithm called Gene
                 Expression Programming (a variant of Genetic
                 Programming) when used with a fuzzy membership within
                 the fitness function are reported.",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, fuzzy-evolutionary
                 computational proteomics approach, liquid
                 chromatography coupled mass spectrometry, mathematical
                 function, nanoLC-MS, nanoLC-MS data, nonlinear
                 retention time shifts problem, biocomputing,
                 evolutionary computation, fuzzy set theory, proteins,
                 proteomics",
  DOI =          "doi:10.1109/CIBCB.2010.5510688",
  notes =        "Also known as \cite{5510688}",
}

Genetic Programming entries for Alan J Barton

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