A parthenogenetic algorithm for single individual SNP haplotyping Academic Article

abstract

• The minimum error correction (MEC) model is one of the important computational models for single individual single nucleotide polymorphism (SNP) haplotyping. Due to the NP-hardness of the model, Qian et al. presented a particle swarm optimization (PSO) algorithm to solve it, and the particle code length is equal to the number of SNP fragments. However, there are hundreds and thousands of SNP fragments in practical applications. The PSO algorithm based on this kind of long particle code cannot obtain high reconstruction rate efficiently. In this paper, a practical heuristic algorithm PGA-MEC based on parthenogenetic algorithm (PGA) is presented to solve the model. A kind of short chromosome code and an effective recombination operator are designed for the algorithm. The reconstruction rate of PGA-MEC algorithm is higher than that of PSO algorithm and the running time of PGA-MEC algorithm is shorter than that of PSO algorithm, which are proved by a number of experiments. 2008 Elsevier Ltd. All rights reserved.

authors

• Chen, Jianer

published proceedings

• ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

author list (cited authors)

• Wu, J., Wang, J., & Chen, J.

citation count

• 8

complete list of authors

• Wu, Jingli||Wang, Jianxin||Chen, Jian’er

publication date

• April 2009

publisher

• Elsevier  Publisher

published in

• Engineering Applications of Artificial Intelligence  Journal

keywords

• Genetic Operator
• Haplotype
• Parthenogenetic Algorithm
• Single Nucleotide Polymorphisms
• The Minimum Error Correction

Digital Object Identifier (DOI)

• 10.1016/j.engappai.2008.09.002

start page

• 401

end page

• 406

volume

• 22

issue

• 3

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.engappai.2008.09.002