HBC-Evo: predicting human breast cancer by exploiting amino acid sequence-based feature spaces and evolutionary ensemble system

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@Article{Majid:2015:AA,
  author =       "Abdul Majid and Safdar Ali",
  title =        "HBC-Evo: predicting human breast cancer by exploiting
                 amino acid sequence-based feature spaces and
                 evolutionary ensemble system",
  year =         "2015",
  journal =      "Amino Acids",
  volume =       "47",
  number =       "1",
  pages =        "217--221",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, Breast cancer
                 diagnosis, Protein sequences, Amino acids,
                 Physicochemical properties, Evolutionary ensemble
                 system, PSO",
  language =     "English",
  publisher =    "Springer",
  ISSN =         "0939-4451",
  URL =          "http://dx.doi.org/10.1007/s00726-014-1871-3",
  DOI =          "doi:10.1007/s00726-014-1871-3",
  size =         "5 pages",
  abstract =     "We developed genetic programming (GP)-based
                 evolutionary ensemble system for the early diagnosis,
                 prognosis and prediction of human breast cancer. This
                 system has effectively exploited the diversity in
                 feature and decision spaces. First, individual learners
                 are trained in different feature spaces using
                 physicochemical properties of protein amino acids.
                 Their predictions are then stacked to develop the best
                 solution during GP evolution process. Finally, results
                 for HBC-Evo system are obtained with optimal threshold,
                 which is computed using particle swarm optimization.
                 Our novel approach has demonstrated promising results
                 compared to state of the art approaches.",
  notes =        "PMID: 25488423 [PubMed - indexed for MEDLINE]",
}

Genetic Programming entries for Abdul Majid Safdar Ali

Citations