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Improved Inference of Gene Regulatory Networks through Integrated Bayesian Clustering and Dynamic Modeling of Time-Course Expression Data

机译：通过集成贝叶斯聚类和时间过程表达数据的动态建模改进了基因调控网络的推断

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摘要

Inferring gene regulatory networks from expression data is difficult, but it is common and often useful. Most network problems are under-determined–there are more parameters than data points–and therefore data or parameter set reduction is often necessary. Correlation between variables in the model also contributes to confound network coefficient inference. In this paper, we present an algorithm that uses integrated, probabilistic clustering to ease the problems of under-determination and correlated variables within a fully Bayesian framework. Specifically, ours is a dynamic Bayesian network with integrated Gaussian mixture clustering, which we fit using variational Bayesian methods. We show, using public, simulated time-course data sets from the DREAM4 Challenge, that our algorithm outperforms non-clustering methods in many cases (7 out of 25) with fewer samples, rarely underperforming (1 out of 25), and often selects a non-clustering model if it better describes the data. Source code (GNU Octave) for BAyesian Clustering Over Networks (BACON) and sample data are available at: .

机译：从表达数据推断基因调控网络是困难的，但它是普遍的且经常有用的。大多数网络问题尚不确定，参数多于数据点，因此通常需要减少数据或参数集。模型中变量之间的相关性也有助于混淆网络系数。在本文中，我们提出一种算法，该算法使用集成的概率聚类来缓解完全贝叶斯框架内欠定性和相关变量的问题。具体来说，我们是一个动态贝叶斯网络，具有集成的高斯混合聚类，我们使用变分贝叶斯方法进行拟合。我们使用DREAM4挑战赛的公共模拟时程数据集显示，在很多情况下（25个中的7个）样本较少，我们的算法表现优于非聚类方法，很少表现不佳（25个中的1个），并且经常选择非聚集模型（如果它可以更好地描述数据）。贝叶斯网络集群（BACON）的源代码（GNU Octave）和示例数据可在以下位置获得：。

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期刊名称 other
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Brian Godsey;
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年(卷),期 -1(8),7
年度 -1
页码 e68358
总页数 6
原文格式 PDF
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专利

1. A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection [J] . Carey Michelle, Ramirez Juan Camilo, Wu Shuang, Statistical methods in medical research . 2018,第7期

机译：一个大数据管道：用时间课程基因表达综合征鉴定动态基因调节网络，其与流感感染的应用
2. Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis [J] . Li Zheng, Li Ping, Krishnan Arun, Bioinformatics . 2011,第19期

机译：将微分方程模型与局部动态贝叶斯网络分析相结合的大规模动态基因调控网络推论
3. Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian networkn analysis [J] . Jingdong Liu Bioinformatics . 2011,第19期

机译：将微分方程模型与局部动态贝叶斯网络分析相结合的大规模动态基因调控网络推论
4. Model-based clustering with genes expression dynamics for time-course gene expression data [C] . Fang-Xiang Wu, Wen-Jun Zhang, Kusalik, . 2004

机译：基于模型的聚类，带有时域基因表达数据的基因表达动力学
5. Gene regulatory network reconstruction and pathway inference from high throughput gene expression data. [D] . Luo, Weijun. 2008

机译：从高通量基因表达数据进行基因调控网络重建和途径推断。
6. Correction: Improved Inference of Gene Regulatory Networks through Integrated Bayesian Clustering and Dynamic Modeling of Time-Course Expression Data [O] . Brian Godsey -1

机译：校正：通过集成贝叶斯聚类和时间过程表达数据的动态建模改进了基因调控网络的推断
7. Improved inference of gene regulatory networks through integrated Bayesian clustering and dynamic modeling of time-course expression data. [O] . Brian Godsey 2013

机译：通过集成贝叶斯聚类和时程表达数据的动态建模改进基因调控网络的推断。

Improved Inference of Gene Regulatory Networks through Integrated Bayesian Clustering and Dynamic Modeling of Time-Course Expression Data

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