Haplotype inference using a novel binary particle swarm optimization algorithm |
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| Affiliation: | 1. Computer Science and Communications Research Unit, University of Luxembourg, Luxembourg;2. Laboratoire d’Informatique Fondamentale de Lille, University of Lille 1, France;1. Department of Applied Mathematics, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong, China;2. School of Electronics and Computer Science, University of Southampton Malaysia Campus, Nusajaya, Johor, Malaysia;3. Department of Electrical and Computer Engineering, Curtin University, WA, Australia;1. South Pars Gas Complex, Bushehr, Iran;2. Department of Electrical and Electronic Engineering, Shahid Bahonar University, Kerman, Iran |
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| Abstract: | The knowledge of haplotypes allows researchers to identify the genetic variation affecting phenotypic such as health, disease and response to drugs. However, getting haplotype data by experimental methods is both time-consuming and expensive. Haplotype inference (HI) from the genotypes is a challenging problem in the genetics domain. There are several models for inferring haplotypes from genotypes, and one of the models is known as haplotype inference by pure parsimony (HIPP) which aims to minimize the number of distinct haplotypes used. The HIPP was proved to be an NP-hard problem. In this paper, a novel binary particle swarm optimization (BPSO) is proposed to solve the HIPP problem. The algorithm was tested on variety of simulated and real data sets, and compared with some current methods. The results showed that the method proposed in this paper can obtain the optimal solutions in most of the cases, i.e., it is a potentially powerful method for HIPP. |
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| Keywords: | Haplotype inference Pure parsimony Genotypes Binary particle swarm optimization |
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