GPMS: A Genetic Programming Based Approach to Multiple Alignment of Liquid Chromatography-Mass Spectrometry Data

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Soha Ahmed and Mengjie Zhang and Lifeng Peng",
  title =        "GPMS: A Genetic Programming Based Approach to Multiple
                 Alignment of Liquid Chromatography-Mass Spectrometry
  booktitle =    "17th European Conference on the Applications of
                 Evolutionary Computation",
  year =         "2014",
  editor =       "Anna Isabel Esparcia-Alcazar and Antonio Miguel Mora",
  series =       "LNCS",
  volume =       "8602",
  publisher =    "Springer",
  pages =        "915--927",
  address =      "Granada",
  month =        "23-25 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-662-45522-7",
  DOI =          "doi:10.1007/978-3-662-45523-4_74",
  abstract =     "Alignment of samples from Liquid chromatography-mass
                 spectrometry (LC-MS) measurements has a significant
                 role in the detection of biomarkers and in metabolomic
                 studies.The machine drift causes differences between
                 LC-MS measurements, and an accurate alignment of the
                 shifts introduced to the same peptide or metabolite is
                 needed. In this paper, we propose the use of genetic
                 programming (GP) for multiple alignment of LC-MS data.
                 The proposed approach consists of two main phases. The
                 first phase is the peak matching where the peaks from
                 different LC-MS maps (peak lists) are matched to allow
                 the calculation of the retention time deviation. The
                 second phase is to use GP for multiple alignment of the
                 peak lists with respect to a reference. In this paper,
                 GP is designed to perform multiple-output regression by
                 using a special node in the tree which divides the
                 output of the tree into multiple outputs. Finally, the
                 peaks that show the maximum correlation after dewarping
                 the retention times are selected to form a consensus
                 aligned map.The proposed approach is tested on one
                 proteomics and two metabolomics LC-MS datasets with
                 different number of samples. The method is compared to
                 several benchmark methods and the results show that the
                 proposed approach outperforms these methods in three
                 fractions of the protoemics dataset and the
                 metabolomics dataset with a larger number of maps.
                 Moreover, the results on the rest of the datasets are
                 highly competitive with the other methods",
  notes =        "EvoApplications2014 held in conjunction with
                 EuroGP'2014, EvoCOP2014, EvoBIO2014, and

Genetic Programming entries for Soha Ahmed Mengjie Zhang Lifeng Peng