Honey bee-inspired algorithms for SNP haplotype reconstruction problem
Reconstructing haplotypes from SNP fragments is an important problem in computational biology. There have been a lot of interests in this field because haplotypes have been shown to contain promising data for disease association research. It is proved that haplotype reconstruction in
Minimum Error Correction model is an NP-hard problem. Therefore, several methods such as clustering techniques, evolutionary algorithms, neural networks and swarm intelligence approaches have been proposed in order to solve this problem in appropriate time. In this paper, we have focused on
various evolutionary clustering techniques and try to find an efficient technique for solving haplotype reconstruction problem. It can be referred from our experiments that the clustering methods relying on the behaviour of honey bee colony in nature, specifically bees algorithm and artificial
bee colony methods, are expected to result in more efficient solutions. An application program of the methods is available at the following link. http://www.bioinf.cs.ipm.ir/software/haprs/
Keywords: bee colony; evolutionary algorithms; haplotype; minimum error correction
Document Type: Research Article
Affiliations: 1: Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran 2: National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
Publication date: 03 March 2016
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