Epi-GTBN: an approach of epistasis mining based on genetic Tabu algorithm and Bayesian network

Background Mining epistatic loci which affects specific phenotypic traits is an important research issue in the field of biology. Bayesian network (BN) is a graphical model which can express the relationship between genetic loci and phenotype. Until now, it has been widely used into epistasis mining...

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Vydané v:BMC bioinformatics Ročník 20; číslo 1; s. 444 - 18
Hlavní autori: Guo, Yang, Zhong, Zhiman, Yang, Chen, Hu, Jiangfeng, Jiang, Yaling, Liang, Zizhen, Gao, Hui, Liu, Jianxiao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 28.08.2019
BioMed Central Ltd
Springer Nature B.V
BMC
Predmet:
Algorithms
Bayesian analysis
Bayesian network
Bioinformatics
Biological evolution
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer simulation
Crossovers
Datasets
Epistasis
Gene loci
Genetic algorithm
Genetic algorithms
Genetic research
Life Sciences
Machine learning
Methodology
Methodology Article
Microarrays
Mining
Mutation
Networks analysis
Optimization
Phenotypes
Search methods
Tabu
Tabu search
China
Epistasis
Genetic algorithm
Bayesian network
Tabu
ISSN:1471-2105, 1471-2105
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Popis
Shrnutí:Background Mining epistatic loci which affects specific phenotypic traits is an important research issue in the field of biology. Bayesian network (BN) is a graphical model which can express the relationship between genetic loci and phenotype. Until now, it has been widely used into epistasis mining in many research work. However, this method has two disadvantages: low learning efficiency and easy to fall into local optimum. Genetic algorithm has the excellence of rapid global search and avoiding falling into local optimum. It is scalable and easy to integrate with other algorithms. This work proposes an epistasis mining approach based on genetic tabu algorithm and Bayesian network ( Epi-GTBN ). It uses genetic algorithm into the heuristic search strategy of Bayesian network. The individual structure can be evolved through the genetic operations of selection, crossover and mutation. It can help to find the optimal network structure, and then further to mine the epistasis loci effectively. In order to enhance the diversity of the population and obtain a more effective global optimal solution, we use the tabu search strategy into the operations of crossover and mutation in genetic algorithm. It can help to accelerate the convergence of the algorithm. Results We compared Epi-GTBN with other recent algorithms using both simulated and real datasets. The experimental results demonstrate that our method has much better epistasis detection accuracy in the case of not affecting the efficiency for different datasets. Conclusions The presented methodology ( Epi-GTBN ) is an effective method for epistasis detection, and it can be seen as an interesting addition to the arsenal used in complex traits analyses.
Bibliografia:ObjectType-Article-1
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-019-3022-z