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

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@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