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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Differential Network Analysis via Weighted Fused Conditional Gaussian Graphical Model
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Differential Network Analysis via Weighted Fused Conditional Gaussian Graphical Model

机译：差分网络分析通过加权融合条件高斯图形模型

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摘要

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.
机译：复杂疾病的发展和预后通常涉及生物分子之间的监管关系的变化。了解如何通过遗传改变改变监管关系可以有助于揭示复杂疾病的潜在生物机制。尽管已经提出了几种模型来估计两个不同状态之间的差分网络，但它们不适合处理感兴趣分子受其他协变量的情况。他们也不能利用先前的信息，这些信息提供了对生物分子网络结构的见解。在这项研究中，我们介绍了一种新型加权融合条件高斯图形模型，共同估计了两个特异性的生物分子调节网络及其两种不同状态之间的差异。与先前的差分网络估计方法不同，我们的模型可以考虑在推断差分网络时的相关协变量和现有网络信息。我们提出模型的有效性是根据仿真研究评估的。实验结果表明，我们的模型在所有情况下都优于其他最先进的差分网络估计模型。然后，我们基于基因表达和miRNA表达数据应用我们的模型以鉴定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已知的胶质母细胞瘤机制并提供有趣的预测。

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