Title: Computational intelligence for genetic association study in complex diseases: review of theory and applications
Authors: Arpad Kelemen, Athanasios V. Vasilakos, Yulan Liang
Addresses: 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
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.
Keywords: computational intelligence; SNP; single nucleotide polymorphisms; epistasis; common complex diseases; genetic association; genetic variations; human genome research; disease mapping; haplotype data.
International Journal of Computational Intelligence in Bioinformatics and Systems Biology, 2009 Vol.1 No.1, pp.15 - 31
Available online: 24 Mar 2009 *Full-text access for editors Access for subscribers Free access Comment on this article