Computational intelligence for genetic association study in complex diseases: review of theory and applications

Authors:
Arpad Kelemen;Athanasios V. Vasilakos;Yulan Liang
Affiliations:
Department of Organisational Systems and Adult Health, University of Maryland, 655 W. Lombard. St., Rm 475A, Baltimore, MD, 21201, USA.;Department of Computer and Telecommunications Engineering, University of Western Macedonia, 50100 Kozani, Greece.;Department of Family and Community Health, University of Maryland, 655 W. Lombard. St., Rm 404K, Baltimore, MD, 21201, USA
Venue:
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Year:
2009

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Abstract

Comprehensive evaluation of common genetic variations through association of SNP structure with common complex disease in the genome-wide scale is currently a hot area in human genome research thanks for the recent development of the Human Genome and HapMap Projects. Computational science, which includes computational intelligence, has recently become the third method of scientific enquiry besides theory and experimentation. There have been fast growing interests in developing and applying computational intelligence in disease mapping using SNP and haplotype data. This review provides coverage of recent developments of theory and applications in computational intelligence for complex diseases in genetic association study.