Machine Learning
Ant algorithms for discrete optimization
Artificial Life
A greedier approach for finding tag SNPs
Bioinformatics
Efficient algorithms for genome-wide association study
ACM Transactions on Knowledge Discovery from Data (TKDD)
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Inferring combined CNV/SNP haplotypes from genotype data
Bioinformatics
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
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The association studies between complex diseases and single nucleotide polymorphisms (SNPs) or haplotypes have recently received great attention. However, these studies are limited by the cost of genotyping all SNPs. Therefore, it is essential to find a small subset of tag SNPs representing the rest of the SNPs. The presence of linkage disequilibrium between tag SNPs and the disease variant (genotyped or not), may allow fine mapping study. In this paper, we combine a nearest-means classifier (NMC) and ant colony algorithm to select tags. Results show that our method (ACO/NMC) can get a similar prediction accuracy with method BPSO/SVM and is better than BPSO/STAMPA for small data sets. For large data sets, although the prediction accuracy of our method is lower than BPSO/SVM, ACO/ NMC can reach a high accuracy ({}99 percent) in a relatively short time. when the number of tags increases, the time complexity of NMC is nearly linear growth. To find out that the ability of tags to locate disease locus, we simulate a case-control study and use two-locus haplotype analysis to quantitatively assess the power. The result showed that 20 percent of all SNPs selected by NMC have about 10 percent higher power than random tags, on average.