How to mine the gene regulatory relationship and construct gene regulatory network (GRN) is of utmost interest within the whole biological community, however, which has been consistently a challenging problem since the tremendous complexity in cellular systems. In present work, we construct gene regulatory network using an improved three-phase dependency analysis algorithm (TPDA) Bayesian network learning method, which includes the steps of Drafting, Thickening, and Thinning. In order to solve the problem of learning result is not reliable due to the high order conditional independence test, we use the entropy estimation approach of Gaussian kernel probability density estimator to calculate the (conditional) mutual information between genes. The experiment on the public benchmark data sets show the improved method outperforms the other nine kinds of Bayesian network learning methods when to process the data with large sample size, with small number of discrete values, and the frequency of different discrete values is about same. In addition, the improved TPDA method was further applied on a real large gene expression data set on RNA-seq from a global collection with 368 elite maize inbred lines. Experiment results show it performs better than the original TPDA method and the other nine kinds of Bayesian network learning algorithms significantly.

中文翻译：

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使用改进的三相相依性分析方法进行基因调控关系挖掘。

然而，如何挖掘基因调控关系并构建基因调控网络（GRN）是整个生物界最为关注的问题，由于细胞系统的巨大复杂性，这一直是一个具有挑战性的问题。在目前的工作中，我们使用改进的三相依赖性分析算法（TPDA）贝叶斯网络学习方法构建基因调控网络，该方法包括绘制，加厚和细化步骤。为了解决由于高阶条件独立性测试导致学习结果不可靠的问题，我们使用高斯核概率密度估计器的熵估计方法来计算基因之间的（条件）互信息。在公开基准数据集上进行的实验表明，该改进方法在处理样本量大，离散值数量少，不同离散值的频率大致相同时，优于其他九种贝叶斯网络学习方法。此外，改进的TPDA方法还应用于来自368个优良玉米自交系的全球采集的RNA-seq上的真实大型基因表达数据集。实验结果表明，与传统的TPDA方法和其他九种贝叶斯网络学习算法相比，该方法具有更好的性能。改进的TPDA方法进一步应用于来自368个优良玉米自交系的全球采集的RNA-seq上的真实大型基因表达数据集。实验结果表明，与传统的TPDA方法和其他九种贝叶斯网络学习算法相比，该方法具有更好的性能。改进的TPDA方法进一步应用于来自368个优良玉米自交系的全球采集的RNA-seq上的真实大型基因表达数据集。实验结果表明，与传统的TPDA方法和其他九种贝叶斯网络学习算法相比，该方法具有更好的性能。