Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining
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 dependenc...
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Series: | BioMed Research International |
Online Access: | http://dx.doi.org/10.1155/2017/1813494 |
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doaj-33ba690b04774cb4bf4f9548baccdb372020-11-25T00:29:55ZengHindawi LimitedBioMed Research International2314-61332314-61412017-01-01201710.1155/2017/18134941813494Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene MiningJianxiao Liu0Zonglin Tian1College of Informatics, Huazhong Agricultural University, Wuhan 430072, ChinaSchool of Computer Science & Engineering, Northeastern University, Shenyang 110000, ChinaBackground 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.http://dx.doi.org/10.1155/2017/1813494 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jianxiao Liu Zonglin Tian |
spellingShingle |
Jianxiao Liu Zonglin Tian Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining BioMed Research International |
author_facet |
Jianxiao Liu Zonglin Tian |
author_sort |
Jianxiao Liu |
title |
Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining |
title_short |
Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining |
title_full |
Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining |
title_fullStr |
Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining |
title_full_unstemmed |
Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining |
title_sort |
verification of three-phase dependency analysis bayesian network learning method for maize carotenoid gene mining |
publisher |
Hindawi Limited |
series |
BioMed Research International |
issn |
2314-6133 2314-6141 |
publishDate |
2017-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2017/1813494 |
work_keys_str_mv |
AT jianxiaoliu verificationofthreephasedependencyanalysisbayesiannetworklearningmethodformaizecarotenoidgenemining AT zonglintian verificationofthreephasedependencyanalysisbayesiannetworklearningmethodformaizecarotenoidgenemining |
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1725328992553140224 |