Induction of fuzzy rules with artificial immune systems in acgh based er status breast cancer characterization

Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Volumn , Issue , 2007, Pages 431-

  Menolascina, Filippo ^a,b   Alves, Roberto Teixeira ^c   Tommasi, Stefania ^a   Chiarappa, Patrizia ^a   Delgado, Myriam ^c   Mastronardi, Giuseppe ^b   Paradiso, Angelo ^a   Freitas, Alex ^d   Bevilacqua, Vitoantonio ^b  

^a NATIONAL CANCER INSTITUTE GIOVANNI PAOLO II   (Italy)

^b POLITECNICO DI BARI   (Italy)

^c UNIVERSIDADE TECNOLÓGICA FEDERAL DO PARANÁ   (Brazil)

^d UNIVERSITY OF KENT   (United Kingdom)

Author keywords

ACGH; AIS; Breast cancer; Data mining; Fuzzy rules induction; IFRAIS

Indexed keywords

DATA MINING; DNA; FUZZY RULES; GENETIC ENGINEERING; IMMUNOLOGY; TUMORS;

ARTIFICIAL IMMUNE SYSTEM (AIS); BREAST CANCER; FUZZY RULES INDUCTION; STATISTICAL ASSESSMENTS;

ARTIFICIAL INTELLIGENCE;

EID: 34548084913     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1276958.1277051     Document Type: Conference Paper

References (4)

1. Alves, RT., Delgado, MR., Lopes, HS., Freitas, AA. An artificial immune system for fuzzy-rule induction in data mining. (PPSN04), LNCS 3242: 1011-1020, 2004.
2. Barry R. Zeeberg, et al, GoMiner: A Resource for Biological Interpretation of Genomic and Proteomic Data. Genome Biology, 2003 4(4):R28
3. Albertson DG. et al, Quantitative mapping of amplicon structure by array CGH identifies CYP24 as a candidate oncogene, Nature Genetics 25, 144-146 (2000)
4. Supplementary Material: http://oncologico.bari.it/laboratorio/ bioinformaticsnews/