scholarly journals Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Jianxiao Liu ◽  
Zonglin Tian
Keyword(s):  
Bayesian Network ◽  
Network Structure ◽  
Structure Learning ◽  
Learning Method ◽  
Dependency Analysis ◽  
Learning Methods ◽  
Network Learning ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning ◽  
Three Phase

Background and Objective. Mining the genes related to maize carotenoid components is important to improve the carotenoid content and the quality of maize. Methods. On the basis of using the entropy estimation method with Gaussian kernel probability density estimator, we use the three-phase dependency analysis (TPDA) Bayesian network structure learning method to construct the network of maize gene and carotenoid components traits. Results. In the case of using two discretization methods and setting different discretization values, we compare the learning effect and efficiency of 10 kinds of Bayesian network structure learning methods. The method is verified and analyzed on the maize dataset of global germplasm collection with 527 elite inbred lines. Conclusions. The result confirmed the effectiveness of the TPDA method, which outperforms significantly another 9 kinds of Bayesian network learning methods. It is an efficient method of mining genes for maize carotenoid components traits. The parameters obtained by experiments will help carry out practical gene mining effectively in the future.

An Algorithm for Learning the Skeleton of Large Bayesian Network

2015 ◽  
Vol 24 (04) ◽  
pp. 1550012
Author(s):  
Yanying Li ◽  
Youlong Yang ◽  
Wensheng Wang ◽  
Wenming Yang
Keyword(s):  
Bayesian Network ◽  
Structure Learning ◽  
State Of The Art ◽  
Large Network ◽  
Dependency Analysis ◽  
Learning From Data ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning ◽  
Np Hard Problem ◽  
Key Steps

It is well known that Bayesian network structure learning from data is an NP-hard problem. Learning a correct skeleton of a DAG is the foundation of dependency analysis algorithms for this problem. Considering the unreliability of the high order condition independence (CI) tests and the aim to improve the efficiency of a dependency analysis algorithm, the key steps are to use less number of CI tests and reduce the sizes of condition sets as many as possible. Based on these analyses and inspired by the algorithm HPC, we present an algorithm, named efficient hybrid parents and child (EHPC), for learning the adjacent neighbors of every variable. We proof the validity of the algorithm. Compared with state-of-the-art algorithms, the experimental results show that EHPC can handle large network and has better accuracy with fewer number of condition independence tests and smaller size of conditioning set.

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