Classification of human cancer diseases by gene expression profiles

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@Article{Salem:2017:ASC,
  author =       "Hanaa Salem and Gamal Attiya and Nawal El-Fishawy",
  title =        "Classification of human cancer diseases by gene
                 expression profiles",
  journal =      "Applied Soft Computing",
  volume =       "50",
  pages =        "124--134",
  year =         "2017",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2016.11.026",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494616305956",
  abstract =     "A cancers disease in virtually any of its types
                 presents a significant reason behind death surrounding
                 the world. In cancer analysis, classification of varied
                 tumor types is of the greatest importance. Microarray
                 gene expressions datasets investigation has been seemed
                 to provide a successful framework for revising tumor
                 and genetic diseases. Despite the fact that standard
                 machine learning ML strategies have effectively been
                 valuable to realize significant genes and classify
                 category type for new cases, regular limitations of DNA
                 microarray data analysis, for example, the small size
                 of an instance, an incredible feature number, yet
                 reason for limitation its investigative, medical and
                 logical uses. Extending the interpretability of
                 expectation and forecast approaches while holding a
                 great precision would help to analysis genes expression
                 profiles information in DNA microarray dataset all the
                 most reasonable and proficiently. This paper presents a
                 new methodology based on the gene expression profiles
                 to classify human cancer diseases. The proposed
                 methodology combines both Information Gain (IG) and
                 Standard Genetic Algorithm (SGA). It first uses
                 Information Gain for feature selection, then uses
                 Genetic Algorithm (GA) for feature reduction and
                 finally uses Genetic Programming (GP) for cancer types'
                 classification. The suggested system is evaluated by
                 classifying cancer diseases in seven cancer datasets
                 and the results are compared with most latest
                 approaches. The use of proposed system on cancers
                 datasets matching with other machine learning
                 methodologies shows that no classification technique
                 commonly outperforms all the others, however, Genetic
                 Algorithm improve the classification performance of
                 other classifiers generally.",
  keywords =     "genetic algorithms, genetic programming, Cancer
                 diagnosis/classification, DNA microarray, Feature
                 selection, Gene expression, Information gain, Machine
                 learning",
}

Genetic Programming entries for Hanaa Salem Gamal Mahrous Ali Attiya Nawal Ahmed El-Fishawy

Citations