The analysis of microarray datasets using a genetic programming

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

@InProceedings{Xu:2009:CIBCB,
  author =       "Chun-Gui Xu and Kun-Hong Liu and De-Shuang Huang",
  title =        "The analysis of microarray datasets using a genetic
                 programming",
  booktitle =    "IEEE Symposium on Computational Intelligence in
                 Bioinformatics and Computational Biology, CIBCB '09",
  year =         "2009",
  month =        "30 " # mar # "-" # apr # " 2",
  pages =        "176--181",
  keywords =     "genetic algorithms, genetic programming, ANN, SVM,
                 artificial neural networks, data classification,
                 disease biomarker search, disease diagnoses, feature
                 selection, gene expression data, gene regulatory
                 network analysis, generated classification rules,
                 informatics tools, microarray dataset analysis,
                 microarray technology, support vector machines, biology
                 computing, feature extraction, genomics, medical
                 computing, molecular biophysics, neural nets, pattern
                 classification, support vector machines",
  DOI =          "doi:10.1109/CIBCB.2009.4925725",
  abstract =     "Microarray technology has been widely applied to
                 search for biomarkers of diseases, diagnose diseases
                 and analyze gene regulatory network. Abundance of
                 expression data from microarray experiments are
                 processed by informatics tools, such as supporting
                 vector machines (SVM), artificial neural network (ANN),
                 and so on. These methods achieve good results in single
                 dataset. Nevertheless, most analyses of microarray data
                 are only focused on a series of data obtained from the
                 same lab or gene chip. Then the discoveries may only be
                 suitable for data they experimented on but lack of
                 general sense. In this paper, we propose a genetic
                 programming (GP) based approach to analyze microarray
                 datasets. The GP implements classification and feature
                 selection at the same time. To validate the
                 significance of the selected genes and generated
                 classification rules, the results are tested on
                 different datasets obtained from different experimental
                 conditions. The results confirm the efficiency of GP in
                 the classification of different samples.",
  notes =        "Also known as \cite{4925725}",
}

Genetic Programming entries for Chun-Gui Xu Kun-Hong Liu De-Shuang Huang

Citations