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Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information

机译:基于条件互信息的路径一致性算法从基因表达数据推断基因调控网络

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Motivation: Reconstruction of gene regulatory networks (GRNs), which explicitly represent the causality of developmental or regulatory process, is of utmost interest and has become a challenging computational problem for understanding the complex regulatory mechanisms in cellular systems. However, all existing methods of inferring GRNs from gene expression profiles have their strengths and weaknesses. In particular, many properties of GRNs, such as topology sparseness and non-linear dependence, are generally in regulation mechanism but seldom are taken into account simultaneously in one computational method. Results: In this work, we present a novel method for inferring GRNs from gene expression data considering the non-linear dependence and topological structure of GRNs by employing path consistency algorithm (PCA) based on conditional mutual information (CMI). In this algorithm, the conditional dependence between a pair of genes is represented by the CMI between them. With the general hypothesis of Gaussian distribution underlying gene expression data, CMI between a pair of genes is computed by a concise formula involving the covariance matrices of the related gene expression profiles. The method is validated on the benchmark GRNs from the DREAM challenge and the widely used SOS DNA repair network in Escherichia coli. The cross-validation results confirmed the effectiveness of our method (PCA-CMI), which outperforms significantly other previous methods. Besides its high accuracy, our method is able to distinguish direct (or causal) interactions from indirect associations.
机译:动机:重建基因调控网络(GRN)的明确表示发展或调控过程的因果关系,是引起人们极大兴趣的问题,并且已成为理解细胞系统中复杂调控机制的具有挑战性的计算问题。但是,所有现有的从基因表达谱推断GRN的方法都有其优点和缺点。特别是,GRN的许多属性(例如拓扑稀疏性和非线性相关性)通常在调节机制中,但是在一种计算方法中很少同时考虑这些属性。结果:在这项工作中,我们提出了一种新的方法,该方法通过使用基于条件互信息(CMI)的路径一致性算法(PCA),考虑到GRN的非线性依赖性和拓扑结构,从基因表达数据中推断GRN。在该算法中,一对基因之间的条件依赖性由它们之间的CMI表示。根据基因表达数据所依据的高斯分布的一般假设,一对基因之间的CMI通过涉及相关基因表达谱的协方差矩阵的简洁公式来计算。该方法在来自DREAM挑战的基准GRN和大肠杆菌中广泛使用的SOS DNA修复网络中得到验证。交叉验证的结果证实了我们方法(PCA-CMI)的有效性,该方法明显优于其他先前方法。除了其高准确性外,我们的方法还能够区分间接关联中的直接(或因果)交互。

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