Improving Feature Ranking for Biomarker Discovery in Proteomics Mass Spectrometry Data using Genetic Programming

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

@Article{Ahmed:2014:CS,
  author =       "Soha Ahmed and Mengjie Zhang and Lifeng Peng",
  title =        "Improving Feature Ranking for Biomarker Discovery in
                 Proteomics Mass Spectrometry Data using Genetic
                 Programming",
  journal =      "Connection Science",
  year =         "2014",
  volume =       "26",
  number =       "3",
  pages =        "215--243",
  keywords =     "genetic algorithms, genetic programming, biomarker
                 discovery, feature selection, classification",
  ISSN =         "0954-0091",
  DOI =          "doi:10.1080/09540091.2014.906388",
  size =         "29 pages",
  abstract =     "Feature selection on mass spectrometry (MS) data is
                 essential for improving classification performance and
                 biomarker discovery. The number of MS samples is
                 typically very small compared with the high
                 dimensionality of the samples, which makes the problem
                 of biomarker discovery very hard. In this paper, we
                 propose the use of genetic programming for biomarker
                 detection and classification of MS data. The proposed
                 approach is composed of two phases: in the first phase,
                 feature selection and ranking are performed. In the
                 second phase, classification is performed. The results
                 show that the proposed method can achieve better
                 classification performance and biomarker detection rate
                 than the information gain (IG) based and the RELIEF
                 feature selection methods. Meanwhile, four classifiers,
                 Naive Bayes, J48 decision tree, random forest and
                 support vector machines, are also used to further test
                 the performance of the top ranked features. The results
                 show that the four classifiers using the top ranked
                 features from the proposed method achieve better
                 performance than the IG and the RELIEF methods.
                 Furthermore, GP also outperforms a genetic algorithm
                 approach on most of the used data sets.",
}

Genetic Programming entries for Soha Ahmed Mengjie Zhang Lifeng Peng

Citations