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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使用改进的三相依赖性分析方法进行基因调控关系挖掘


然而，由于细胞系统的巨大复杂性，如何挖掘基因调控关系并构建基因调控网络（GRN）一直是整个生物界最感兴趣的问题。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，包括Drafting、Thickening和Thinning步骤。为了解决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。在公共基准数据集上的实验表明，改进方法在处理大样本、离散值数量较少的数据时，优于其他9种贝叶斯网络学习方法，且不同离散值出现的频率大致相同。此外，改进的TPDA方法还进一步应用于来自全球368个优良玉米自交系的RNA-seq真实大型基因表达数据集。实验结果表明，其性能明显优于原始TPDA方法和其他9种贝叶斯网络学习算法。