Inferring gene co-expression networks from high-throughput gene expression data is an important task in bioinformatics. Many gene networks often exhibit modular structures. Although several Gaussian graphical model-based methods have been developed to estimate gene co-expression networks by incorporating the modular structural prior, none of them takes into account the modular structures captured by the prior networks (e.g., protein interaction networks). In this study, we propose a novel prior network-dependent gene network inference (pGNI) method to estimate gene co-expression networks by integrating gene expression data and prior protein interaction network data. The underlying modular structure is learned from both sets of data. Through simulation studies, we demonstrate the feasibility and effectiveness of our method. We also apply our method to two real datasets. The modular structures in the networks estimated by our method are biological significant.

中文翻译：

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通过结合先前的蛋白质-蛋白质相互作用网络来推断基因共表达网络


从高通量基因表达数据推断基因共表达网络是生物信息学的一项重要任务。许多基因网络通常表现出模块化结构。尽管已经开发了几种基于高斯图形模型的方法来通过合并模块化结构先验来估计基因共表达网络，但它们都没有考虑先验网络（例如蛋白质相互作用网络）捕获的模块化结构。在本研究中，我们提出了一种新颖的先验网络相关基因网络推理（pGNI）方法，通过整合基因表达数据和先验蛋白质相互作用网络数据来估计基因共表达网络。底层的模块化结构是从两组数据中学习到的。通过仿真研究，我们证明了我们方法的可行性和有效性。我们还将我们的方法应用于两个真实数据集。我们的方法估计的网络中的模块化结构具有生物学意义。