Epi-GTBN: an approach of epistasis mining based on genetic Tabu algorithm and Bayesian network
Epi-GTBN: an approach of epistasis mining based on genetic Tabu algorithm and Bayesian network
复制标题
Epi-GTBN：一种基于遗传禁忌算法和贝叶斯网络的上位挖掘方法
DOI：
10.1186/s12859-019-3022-z
复制
发表时间：
2019-08-28
影响因子：
3
通讯作者：
Liu, Jianxiao
中科院分区：
文献类型：
--
作者：
Guo, Yang;Zhong, Zhiman;Liu, Jianxiao
背景影响特定表型性状的上位性位点的挖掘是生物学领域的一个重要研究课题。贝叶斯网络（BN）是一种能够表达遗传位点与表型之间关系的图形模型。到目前为止，它已被广泛应用于上位性挖掘的许多研究工作。但该方法存在学习效率低和易陷入局部最优两个缺点。遗传算法具有快速全局搜索和避免陷入局部最优的优点。它具有可扩展性，易于与其他算法集成。提出了一种基于遗传禁忌算法和贝叶斯网络的上位性挖掘方法（Epi-GTBN）。将遗传算法引入到贝叶斯网络的启发式搜索策略中。个体结构可以通过选择、交叉和变异等遗传操作来进化。该方法有助于寻找最优网络结构，进而有效地挖掘上位性位点。为了提高种群的多样性，获得更有效的全局最优解，我们将禁忌搜索策略引入遗传算法的交叉和变异操作中。它可以帮助加快收敛的algorithm.ResultsWe比较Epi-GTBN与其他最近的算法使用模拟和真实的数据集。实验结果表明，我们的方法有更好的上位性检测精度的情况下，不影响效率为不同dataset.ConclusionsThe提出的方法（Epi-GTBN）是一种有效的上位性检测方法，它可以被看作是一个有趣的除了在复杂的性状分析中使用的武器库。
BackgroundMining 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.ResultsWe comparedEpi-GTBNwith 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.ConclusionsThe 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.