Fuzzy rule induction and artificial immune systems in female breast cancer familiarity profiling

Authors:
Filippo Menolascina;Roberto T. Alves;Stefania Tommasi;Patrizia Chiarappa;Myriam Delgado;Vitoantonio Bevilacqua;Giuseppe Mastronardi;Alex A. Freitas;Angelo Paradiso
Affiliations:
(Correspd.) Department of Electronics and Electrical Engineering, Polytechnic of Bari, 2 Via E. Orabona 4, 70125. Bari, Italy;Federal Technological University of Paraná - UTFPR, Av. 7 de setembro, 3165. Curitiba, Brazil;Clinical and Experimental Oncology Laboratory - NCI, 1 Via Hahnemann 10, 70126. Bari, Italy;Clinical and Experimental Oncology Laboratory - NCI, 1 Via Hahnemann 10, 70126. Bari, Italy;Federal Technological University of Paraná - UTFPR, Av. 7 de setembro, 3165. Curitiba, Brazil;Department of Electronics and Electrical Engineering, Polytechnic of Bari, 2 Via E. Orabona 4, 70125. Bari, Italy;Department of Electronics and Electrical Engineering, Polytechnic of Bari, 2 Via E. Orabona 4, 70125. Bari, Italy;Computing Laboratory - University of Kent, CT2 7NF. Canterbury, UK;Clinical and Experimental Oncology Laboratory - NCI, 1 Via Hahnemann 10, 70126. Bari, Italy
Venue:
International Journal of Hybrid Intelligent Systems - Recent Advances in Intelligent Paradigms Fusion and Their Applications
Year:
2008

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Abstract

Genomic DNA copy number aberrations are frequent in solid tumours although their underlying causes of chromosomal instability in tumours remain obscure. In this paper we show how Artificial Immune System (AIS) paradigm can be successfully employed in the elucidation of biological dynamics of cancerous processes using a novel fuzzy rule induction system for data mining (IFRAIS) of aCGH data. Competitive results have been obtained using IFRAIS. A biological interpretation of the results carried out using Gene Ontology is currently under investigation.