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:

  • Clinical and Experimental Oncology Laboratory, NCI, Bari, Italy and Department of Electronics and Electrical Engineering, Polytechnic of Bari, Bari, Italy;Federal Technological University of Paranà, UTFPR, Curitiba, Brazil;Clinical and Experimental Oncology Laboratory, NCI, Bari, Italy;Clinical and Experimental Oncology Laboratory, NCI, Bari, Italy;Federal Technological University of Paranà, UTFPR, Curitiba, Brazil;Department of Electronics and Electrical Engineering, Polytechnic of Bari, Bari, Italy;Department of Electronics and Electrical Engineering, Polytechnic of Bari, Bari, Italy;Computing Laboratory, University of Kent, Canterbury, UK;Clinical and Experimental Oncology Laboratory, NCI, Bari, Italy

  • Venue:
  • KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
  • Year:
  • 2007

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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) [1] 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.