Data mining in complex diseases using evolutionary computation

Vanessa Aguiar, Jose A. Seoane, Ana Freire, Cristian R. Munteanu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 similar classification scores but found more rules from the data generated artificially. In this paper it is assumed that several groups of 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 publicationBio-Inspired Systems
Subtitle of host publicationComputational and Ambient Intelligence - 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings
Pages917-924
Number of pages8
EditionPART 1
DOIs
StatePublished - 2009
Externally publishedYes
Event10th International Work-Conference on Artificial Neural Networks, IWANN 2009 - Salamanca, Spain
Duration: Jun 10 2009Jun 12 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5517 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Work-Conference on Artificial Neural Networks, IWANN 2009
Country/TerritorySpain
CitySalamanca
Period6/10/096/12/09

Keywords

  • Association studies
  • Data mining
  • Evolutionary computation
  • Genetic algorithms
  • SNPs

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'Data mining in complex diseases using evolutionary computation'. Together they form a unique fingerprint.

Cite this