Multiple ant colony algorithm method for selecting tag SNPs

Authors:
Bo Liao;Xiong Li;Wen Zhu;Renfa Li;Shulin Wang
Affiliations:
The College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China;The College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China;The College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China;The College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China;The College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China
Venue:
Journal of Biomedical Informatics
Year:
2012

Citing 6
Cited 0

Ant algorithms for discrete optimization

Artificial Life
CLUSTAG: hierarchical clustering and graph methods for selecting tag SNPs

Bioinformatics
A greedier approach for finding tag SNPs

Bioinformatics
Computational intelligence in bioinformatics: SNP/haplotype data in genetic association study for common diseases

IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Multi-objective tag SNPs selection using evolutionary algorithms

Bioinformatics
Tag SNP selection via a genetic algorithm

Journal of Biomedical Informatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

The search for the association between complex disease and single nucleotide polymorphisms (SNPs) or haplotypes has recently received great attention. Finding a set of tag SNPs for haplotyping in a great number of samples is an important step to reduce cost for association study. Therefore, it is essential to select tag SNPs with more efficient algorithms. In this paper, we model problem of selection tag SNPs by MINIMUM TEST SET and use multiple ant colony algorithm (MACA) to search a smaller set of tag SNPs for haplotyping. The various experimental results on various datasets show that the running time of our method is less than GTagger and MLR. And MACA can find the most representative SNPs for haplotyping, so that MACA is more stable and the number of tag SNPs is also smaller than other evolutionary methods (like GTagger and NSGA-II). Our software is available upon request to the corresponding author.