title: Computational intelligence in bioinformatics: SNP/haplotype data in genetic association study for common diseases.
abstract: Comprehensive evaluation of common genetic variations through association of single-nucleotide polymorphism (SNP) structure with common complex disease in the genome-wide scale is currently a hot area in human genome research due to the recent development of the Human Genome Project and HapMap Project. Computational science, which includes computational intelligence (CI), has recently become the third method of scientific enquiry besides theory and experimentation. There have been fast growing interests in developing and applying CI in disease mapping using SNP and haplotype data. Some of the recent studies have demonstrated the promise and importance of CI for common complex diseases in genomic association study using SNP/haplotype data, especially for tackling challenges, such as gene-gene and gene-environment interactions, and the notorious "curse of dimensionality" problem. This review provides coverage of recent developments of CI approaches for complex diseases in genetic association study with SNP/haplotype data.
authors: Department of Organizational Systems and Adult Health, University of Maryland, Baltimore, MD 21201, USA. akele001@umaryland.edu
authors: Arpad
authors: A
authors: Kelemen
authors: Athanasios V
authors: AV
authors: Vasilakos
authors: Yulan
authors: Y
authors: Liang
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D004843:Epistasis, Genetic; D020022:Genetic Predisposition to Disease; D006239:Haplotypes; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide