Analyzing Feature Importance for Metabolomics using Genetic Programming

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

@InProceedings{Hu:2018:EuroGP,
  author =       "Ting Hu and Karoliina Oksanen and Weidong Zhang and 
                 Edward Randell and Andrew Furey and Guangju Zhai",
  title =        "Analyzing Feature Importance for Metabolomics using
                 Genetic Programming",
  booktitle =    "EuroGP 2018: Proceedings of the 21st European
                 Conference on Genetic Programming",
  year =         "2018",
  month =        "4-6 " # apr,
  editor =       "Mauro Castelli and Lukas Sekanina and 
                 Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez",
  series =       "LNCS",
  volume =       "10781",
  publisher =    "Springer Verlag",
  address =      "Parma, Italy",
  pages =        "68--83",
  organisation = "EvoStar, Species",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-77552-4",
  DOI =          "doi:10.1007/978-3-319-77553-1_5",
  abstract =     "The emerging and fast-developing field of metabolomics
                 examines the abundance of small-molecule metabolites in
                 body fluids to study the cellular processes related to
                 how the human body responds to genetic and
                 environmental perturbations. Considering the complexity
                 of metabolism, metabolites and their represented
                 cellular processes can correlate and synergistically
                 contribute to a phenotypic status. Genetic programming
                 (GP) provides advanced analytical instruments for the
                 investigation of multifactorial causes of metabolic
                 diseases. In this article, we analysed a
                 population-based metabolomics dataset on osteoarthritis
                 (OA) and developed a Linear GP (LGP) algorithm to
                 search classification models that can best predict the
                 disease outcome, as well as to identify the most
                 important metabolic markers associated with the
                 disease. The LGP algorithm was able to evolve
                 prediction models with high accuracies especially with
                 a more focused search using a reduced feature set that
                 only includes potentially relevant metabolites. We also
                 identified a set of key metabolic markers that may
                 improve our understanding of the biochemistry and
                 pathogenesis of the disease.",
  notes =        "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in
                 conjunction with EvoCOP2018, EvoMusArt2018 and
                 EvoApplications2018",
}

Genetic Programming entries for Ting Hu Karoliina Oksanen Weidong Zhang Edward Randell Andrew Furey Guangju Zhai

Citations