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GA-Based Data Mining Applied to Genetic Data for the Diagnosis of Complex Diseases

GA-Based Data Mining Applied to Genetic Data for the Diagnosis of Complex Diseases
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Author(s): Vanessa Aguiar (University of A Coruña, Spain), Jose A. Seoane (University of A Coruña, Spain), Ana Freire (University of A Coruña, Spain)and Ling Guo (University of A Coruña, Spain)
Copyright: 2010
Pages: 21
Source title: Soft Computing Methods for Practical Environment Solutions: Techniques and Studies
Source Author(s)/Editor(s): Marcos Gestal Pose (University of A Coruna, Spain)and Daniel Rivero Cebrián (University of A Coruna, Spain)
DOI: 10.4018/978-1-61520-893-7.ch014

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Abstract

A new algorithm is presented for finding genotype-phenotype association rules from data related to complex diseases. The algorithm was based on genetic algorithms, a technique of evolutionary computation. The algorithm was compared to several traditional data mining techniques and it was proved that it obtained better classification scores and found more rules from the data generated artificially. It also obtained similar results when using some UCI Machine Learning datasets. In this chapter it is assumed that several groups of Single Nucleotide Polymorphisms (SNPs) have an impact on the predisposition to develop a complex disease like schizophrenia. It is expected to validate this in a short period of time on real data.

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