Title :
Novel Data Mining Techniques in aCGH based Breast Cancer Subtypes Profiling: the Biological Perspective
Author :
Menolascina, F. ; Tommasi, S. ; Paradiso, A. ; Cortellino, M. ; Bevilacqua, V. ; Mastronardi, G.
Author_Institution :
Clinical & Exp. Oncology Lab., National Cancer Inst., Bari
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
Keywords :
cancer; data mining; learning (artificial intelligence); medical computing; statistical analysis; J48; aCGH based breast cancer subtypes profiling; ant miner; biological perspective; gene expression programming; gene ontology; machine learning; naive Bayes tree; novel data mining; statistical method; Breast cancer; Computational intelligence; Data mining; Gene expression; Intelligent systems; Machine learning; Machine learning algorithms; Ontologies; Pipelines; Statistical analysis; Ant Miner; Breast Caner; Decision Trees; Gene Expression Programming; Rule Induction;
Conference_Titel :
Computational Intelligence and Bioinformatics and Computational Biology, 2007. CIBCB '07. IEEE Symposium on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0710-9
DOI :
10.1109/CIBCB.2007.4221198
