A Multi-objective Genetic Programming Biomarker Detection Approach in Mass Spectrometry Data

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

@InProceedings{conf/evoW/AhmedZPX16,
  author =       "Soha Ahmed and Mengjie Zhang and Lifeng Peng and 
                 Bing Xue",
  title =        "A Multi-objective Genetic Programming Biomarker
                 Detection Approach in Mass Spectrometry Data",
  booktitle =    "19th European Conference on Applications of
                 Evolutionary Computation, EvoApplications 2016",
  year =         "2016",
  editor =       "Giovanni Squillero and Paolo Burelli",
  volume =       "9597",
  series =       "Lecture Notes in Computer Science",
  pages =        "106--122",
  address =      "Porto, Portugal",
  month =        mar # " 30 -- " # apr # " 1",
  organisation = "EvoStar",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  bibdate =      "2016-03-23",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/evoW/evoappl2016-1.html#AhmedZPX16",
  isbn13 =       "978-3-319-31204-0",
  DOI =          "doi:10.1007/978-3-319-31204-0_8",
  abstract =     "Mass spectrometry is currently the most commonly used
                 technology in biochemical research for proteomic
                 analysis. The main goal of proteomic profiling using
                 mass spectrometry is the classification of samples from
                 different clinical states. This requires the
                 identification of proteins or peptides (biomarkers)
                 that are expressed differentially between different
                 clinical states. However, due to the high
                 dimensionality of the data and the small number of
                 samples, classification of mass spectrometry data is a
                 challenging task. Therefore, an effective feature
                 manipulation algorithm either through feature selection
                 or construction is needed to enhance the classification
                 performance and at the same time minimise the number of
                 features. Most of the feature manipulation methods for
                 mass spectrometry data treat this problem as a single
                 objective task which focuses on improving the
                 classification performance. This paper presents two new
                 methods for biomarker detection through multi-objective
                 feature selection and feature construction. The results
                 show that the proposed multi-objective feature
                 selection method can obtain better subsets of features
                 than the single-objective algorithm and two traditional
                 multi-objective approaches for feature selection.
                 Moreover, the multi-objective feature construction
                 algorithm further improves the performance over the
                 multi-objective feature selection algorithm. This paper
                 is the first multi-objective genetic programming
                 approach for biomarker detection in mass spectrometry
                 data",
  notes =        "EvoApplications2016 held inconjunction with
                 EuroGP'2016, EvoCOP2016 and EvoMUSART 2016",
}

Genetic Programming entries for Soha Ahmed Mengjie Zhang Lifeng Peng Bing Xue

Citations