IEEE/ACM Trans Comput Biol Bioinform. 2020 Nov-Dec;17(6):2162-2169. doi: 10.1109/TCBB.2019.2924418. Epub 2020 Dec 8.
The development and prognosis of complex diseases usually involves changes in regulatory relationships among biomolecules. Understanding how the regulatory relationships change with genetic alterations can help to reveal the underlying biological mechanisms for complex diseases. Although several models have been proposed to estimate the differential network between two different states, they are not suitable to deal with situations where the molecules of interest are affected by other covariates. Nor can they make use of prior information that provides insights about the structures of biomolecular networks. In this study, we introduce a novel weighted fused conditional Gaussian graphical model to jointly estimate two state-specific biomolecular regulatory networks and their difference between two different states. Unlike previous differential network estimation methods, our model can take into account the related covariates and the prior network information when inferring differential networks. The effectiveness of our proposed model is first evaluated based on simulation studies. Experiment results demonstrate that our model outperforms other state-of-the-art differential networks estimation models in all cases. We then apply our model to identify the differential gene network between two subtypes of glioblastoma based on gene expression and miRNA expression data. Our model is able to discover known mechanisms of glioblastoma and provide interesting predictions.
复杂疾病的发展和预后通常涉及生物分子之间调控关系的变化。了解调控关系如何随着遗传改变而变化，可以帮助揭示复杂疾病的潜在生物学机制。尽管已经提出了几种模型来估计两个不同状态之间的差异网络，但它们不适合处理感兴趣的分子受到其他协变量影响的情况。它们也不能利用提供有关生物分子网络结构的见解的先验信息。在这项研究中，我们引入了一种新的加权融合条件高斯图形模型，用于联合估计两个特定状态的生物分子调控网络及其在两个不同状态之间的差异。与以前的差异网络估计方法不同，我们的模型在推断差异网络时可以考虑相关协变量和先验网络信息。我们提出的模型的有效性首先通过模拟研究进行评估。实验结果表明，在所有情况下，我们的模型都优于其他最先进的差异网络估计模型。然后，我们应用我们的模型基于基因表达和 miRNA 表达数据来识别两种胶质母细胞瘤亚型之间的差异基因网络。我们的模型能够发现胶质母细胞瘤的已知机制，并提供有趣的预测。
