IEEE/ACM Trans Comput Biol Bioinform. 2022 May-Jun;19(3):1545-1557. doi: 10.1109/TCBB.2020.3034861. Epub 2022 Jun 3.
Previous efforts in gene network reconstruction have mainly focused on data-driven modeling, with little attention paid to knowledge-based approaches. Leveraging prior knowledge, however, is a promising paradigm that has been gaining momentum in network reconstruction and computational biology research communities. This paper proposes two new algorithms for reconstructing a gene network from expression profiles with and without prior knowledge in small sample and high-dimensional settings. First, using tools from the statistical estimation theory, particularly the empirical Bayesian approach, the current research estimates a covariance matrix via the shrinkage method. Second, estimated covariance matrix is employed in the penalized normal likelihood method to select the Gaussian graphical model. This formulation allows the application of prior knowledge in the covariance estimation, as well as in the Gaussian graphical model selection. Experimental results on simulated and real datasets show that, compared to state-of-the-art methods, the proposed algorithms achieve better results in terms of both PR and ROC curves. Finally, the present work applies its method on the RNA-seq data of human gastric atrophy patients, which was obtained from the EMBL-EBI database. The source codes and relevant data can be downloaded from: https://github.com/AbbaszadehO/DKGN.
先前的基因网络重建工作主要集中在数据驱动的建模上，很少关注基于知识的方法。然而，利用先验知识是一种很有前途的范式，在网络重建和计算生物学研究社区中越来越受到关注。本文提出了两种新的算法，用于在小样本和高维环境中从表达谱中重建带有和不带有先验知识的基因网络。首先，利用统计估计理论的工具，特别是经验贝叶斯方法，当前的研究通过收缩方法估计协方差矩阵。其次，在惩罚正态似然方法中使用估计的协方差矩阵来选择高斯图形模型。这种公式允许在先验知识的协方差估计以及高斯图形模型选择中应用先验知识。在模拟和真实数据集上的实验结果表明，与最先进的方法相比，所提出的算法在 PR 和 ROC 曲线方面都取得了更好的结果。最后，本工作将其方法应用于从 EMBL-EBI 数据库获得的人类胃萎缩患者的 RNA-seq 数据。源代码和相关数据可以从以下网址下载：https://github.com/AbbaszadehO/DKGN。
