Novel Data Mining Techniques in aCGH based Breast Cancer Subtypes Profiling: the Biological Perspective

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{Menolascina:2007:CIBCB,
  author =       "F. Menolascina and S. Tommasi and A. Paradiso and 
                 M. Cortellino and V. Bevilacqua and G. Mastronardi",
  title =        "Novel Data Mining Techniques in {aCGH} based Breast
                 Cancer Subtypes Profiling: the Biological Perspective",
  booktitle =    "IEEE Symposium on Computational Intelligence and
                 Bioinformatics and Computational Biology, CIBCB '07",
  year =         "2007",
  pages =        "9--16",
  address =      "Honolulu, USA",
  month =        "1-5 " # apr,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, ant miner, breast caner,
                 decision trees, rule induction",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.628.4889",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.628.4889",
  URL =          "http://s3.amazonaws.com/publicationslist.org/data/bevilacqua/ref-30/cibcib.pdf",
  DOI =          "doi:10.1109/CIBCB.2007.4221198",
  size =         "8 pages",
  abstract =     "In this paper we present a comparative study among
                 well established data mining algorithm (namely J48 and
                 Naive Bayes Tree) and novel machine learning paradigms
                 like Ant Miner and Gene Expression Programming. The aim
                 of this study was to discover significant rules
                 discriminating ER+ and ER-cases of breast cancer. We
                 compared both statistical accuracy and biological
                 validity of the results using common statistical
                 methods and Gene Ontology. Some worth noting
                 characteristics of these systems have been observed and
                 analysed even giving some possible interpretations of
                 findings. With this study we tried to show how
                 intelligent systems can be employed in the design of
                 experimental pipeline in disease processes
                 investigation and how deriving high-throughput results
                 can be validated using new computational tools. Results
                 returned by this approach seem to encourage new efforts
                 in this field.",
}

Genetic Programming entries for Filippo Menolascina S Tommasi A Paradiso M Cortellino Vitoantonio Bevilacqua G Mastronardi

Citations