Liu Jianxiao, Tian Zonglin
College of Informatics, Huazhong Agricultural University, Wuhan 430072, China.
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430072, China.
Biomed Res Int. 2017;2017:1813494. doi: 10.1155/2017/1813494. Epub 2017 Jul 30.
Mining the genes related to maize carotenoid components is important to improve the carotenoid content and the quality of maize.
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.
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.
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.
挖掘与玉米类胡萝卜素成分相关的基因对于提高玉米类胡萝卜素含量和品质具有重要意义。
在使用高斯核概率密度估计器的熵估计方法的基础上，采用三相依赖分析（TPDA）贝叶斯网络结构学习方法构建玉米基因与类胡萝卜素成分性状的网络。
在使用两种离散化方法并设置不同离散化值的情况下，比较了10种贝叶斯网络结构学习方法的学习效果和效率。该方法在包含527个优良自交系的全球种质收集玉米数据集上进行了验证和分析。
结果证实了TPDA方法的有效性，该方法显著优于其他9种贝叶斯网络学习方法。它是一种挖掘玉米类胡萝卜素成分性状基因的有效方法。实验获得的参数将有助于未来有效地进行实际的基因挖掘。
