GA-based data mining applied to genetic data for the diagnosis of complex diseases

Vanessa Aguiar, Jose A. Seoane, Ana Freire, Ling Guo

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationSoft Computing Methods for Practical Environment Solutions
Subtitle of host publicationTechniques and Studies
PublisherIGI Global
Pages219-239
Number of pages21
ISBN (Print)9781615208937
DOIs
StatePublished - 2010
Externally publishedYes

ASJC Scopus subject areas

  • Computer Science(all)

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