Computational Intelligence Techniques: A Study of Scleroderma Skin Disease

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TypeArticle
Proceedings titleThe Genetic and Evolutionary Computation Conference (GECCO-2007)
ConferenceGECCO Late-Breaking Paper, July 7-11, 2007.
Subjectvisual data mining; virtual reality spaces; differential evolution; particle swarm optimization; hybrid evolutionary-classical optimization; similarity structure preservation; clustering; rough sets; genetic programming; grid computing; genomics; Sclerode
AbstractThis paper presents an analysis of microarray gene expression data from patients with and without scleroderma skin disease using computational intelligence and visual data mining techniques. Virtual reality spaces are used for providing unsupervised insight about the information content of the original set of genes describing the objects. These spaces are constructed by hybrid optimization algorithms based on a combination of Differential Evolution (DE) and Particle Swarm Optimization respectively, with deterministic Fletcher-Reeves optimization. A distributed-pipelined data mining algorithm composed of clustering and cross-validated rough sets analysis is applied in order to find subsets of relevant attributes with high classification capabilities. Finally, genetic programming (GP) is applied in order to find explicit analytic expressions for the characteristic functions of the scleroderma and the normal classes. The virtual reality spaces associated with the set of function arguments (genes) are also computed. Several small subsets of genes are discovered which are capable of classifying the data with complete accuracy. They represent genes potentially relevant to the understanding of the scleroderma disease.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number49293
NPARC number8913636
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Record identifierb8012765-9292-40b2-921d-7a499df44ced
Record created2009-04-22
Record modified2016-05-09
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