DocumentCode :
1099178
Title :
Computational Intelligence in Bioinformatics: SNP/Haplotype Data in Genetic Association Study for Common Diseases
Author :
Kelemen, A. ; Vasilakos, A.V. ; Yulan Liang
Author_Institution :
Dept. of Organizational Syst. & Adult Health, Univ. of Maryland, Baltimore, MD, USA
Volume :
13
Issue :
5
fYear :
2009
Firstpage :
841
Lastpage :
847
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.
Keywords :
artificial intelligence; biology computing; diseases; genomics; bioinformatics; common complex disease; computational intelligence; genetic association; genome research; haplotype data; single-nucleotide polymorphism; Bioinformatics; Cardiac disease; Cardiovascular diseases; Computational intelligence; Data analysis; Environmental factors; Genetics; Genomics; Humans; Telecommunication computing; Common complex diseases; computational intelligence (CI); epistasis; single-nucleotide polymorphisms (SNPs); Artificial Intelligence; Computational Biology; Epistasis, Genetic; Genetic Predisposition to Disease; Haplotypes; Humans; Models, Genetic; Polymorphism, Single Nucleotide;
fLanguage :
English
Journal_Title :
Information Technology in Biomedicine, IEEE Transactions on
Publisher :
ieee
ISSN :
1089-7771
Type :
jour
DOI :
10.1109/TITB.2009.2024144
Filename :
5109693
Link To Document :
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