Division of Biostatistics and Bioinformatics, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
Bioinformatics. 2022 Apr 28;38(9):2481-2487. doi: 10.1093/bioinformatics/btac038.
The collection of temporal or perturbed data is often a prerequisite for reconstructing dynamic networks in most cases. However, these types of data are seldom available for genomic studies in medicine, thus significantly limiting the use of dynamic networks to characterize the biological principles underlying human health and diseases.
We proposed a statistical framework to recover disease risk-associated pseudo-dynamic networks (DRDNet) from steady-state data. We incorporated a varying coefficient model with multiple ordinary differential equations to learn a series of networks. We analyzed the publicly available Genotype-Tissue Expression data to construct networks associated with hypertension risk, and biological findings showed that key genes constituting these networks had pivotal and biologically relevant roles associated with the vascular system. We also provided the selection consistency of the proposed learning procedure and evaluated its utility through extensive simulations.
DRDNet is implemented in the R language, and the source codes are available at https://github.com/chencxxy28/DRDnet/.
Supplementary data are available at Bioinformatics online.
在大多数情况下，收集时变或扰动数据通常是重建动态网络的前提。然而，在医学的基因组研究中，很少有这类数据可用，这极大地限制了动态网络在刻画人类健康和疾病背后的生物学原理中的应用。
我们提出了一种从稳态数据中恢复疾病风险相关伪动态网络（DRDNet）的统计框架。我们将一个具有多个常微分方程的时变系数模型纳入其中，以学习一系列网络。我们分析了公开的基因型组织表达数据，构建了与高血压风险相关的网络，生物学研究结果表明，构成这些网络的关键基因与血管系统有重要的、具有生物学相关性的作用。我们还提供了所提出的学习过程的选择一致性，并通过广泛的模拟评估了其效用。
DRDNet 是用 R 语言实现的，其源代码可在 https://github.com/chencxxy28/DRDnet/ 上获得。
补充数据可在《生物信息学》在线获得。
