Novel data mining techniques in aCGH based breast cancer subtypes profiling: The biological perspective

F. Menolascina, S. Tommasi, A. Paradiso, M. Cortellino, V. Bevilacqua, G. Mastronardi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we present a comparative study among well established data mining algorithm (namely J48 and Naïve 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 ERcases 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.

Original languageEnglish
Title of host publication2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB 2007
Pages9-16
Number of pages8
Publication statusPublished - 2007
Event2007 4th IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2007 - Honolulu, HI, United States
Duration: Apr 1 2007Apr 5 2007

Other

Other2007 4th IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2007
Country/TerritoryUnited States
CityHonolulu, HI
Period4/1/074/5/07

Keywords

  • Ant Miner
  • Breast Caner
  • Decision Trees
  • Gene Expression Programming
  • Rule Induction

ASJC Scopus subject areas

  • Artificial Intelligence
  • Biomedical Engineering
  • Health Informatics

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