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

Jianxiao Liu,Zonglin Tian

doi:10.1155/2017/1813494

Abstract

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.

Highlights

Background and ObjectiveMining the genes related to maize carotenoid components is important to improve the carotenoid content and the quality of maize
About 250,000–500,000 children in the world suffer from blindness each year owing to vitamin A deficiency [1], which is an urgent problem to be solved at present
The bnlearn is an R package for learning the graphical structure of Bayesian network, estimating the parameters and performing some useful Bayesian inference. This package provides a number of underlying libraries about Bayesian network learning, including structure learning, parameter learning, and inference

Summary

Introduction

Mining the genes related to maize carotenoid components is important to improve the carotenoid content and the quality of maize. 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. Mining the genes related to maize carotenoid components and improving the content of vitamin A through genomic methods are some of the economic and effective ways to solve the problem of vitamin A deficiency. How to use the bioinformatics methods to mine the genes for carotenoid components from these massive data is important to improve the carotenoid content and the quality of maize

Methods

Results

Conclusion