利用向量自回归和模型选择的自动相关性确定来推断具有调控枢纽的遗传调控网络。

IF 0.9 4区 数学 Q3 Mathematics
Chi-Kan Chen
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引用次数: 1

摘要

遗传调控网络(grn)的推断揭示了基因之间如何相互作用。少数基因可以调控许多基因作为靶标来控制细胞功能。我们提出了基于order-1向量自回归(VAR1)的新方法,用于从基因表达时间序列推断grn。该方法使用自动相关性确定(ARD)将监管枢纽结构纳入贝叶斯框架中VAR1的估计。将几种稀疏逼近方案应用于估计的回归权值或VAR1模型,生成表示推断grn的稀疏加权邻接矩阵。我们利用模拟的DREAM4和实验的大肠杆菌基因表达时间序列,应用所提出的方法和几种广泛的参考方法来推断多达100个基因的grn。研究表明,该方法在模拟轮毂grn和使用var1模拟的短时间序列的无标度grn上是有效的,并且在小型DREAM4硅grn和大肠杆菌grn上优于参考方法。他们可以利用已知的主要调控中心来提高更大的DREAM4硅grn和大肠杆菌grn的性能。讨论了非线性时间序列数据对所提方法性能的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inference of genetic regulatory networks with regulatory hubs using vector autoregressions and automatic relevance determination with model selections.

The inference of genetic regulatory networks (GRNs) reveals how genes interact with each other. A few genes can regulate many genes as targets to control cell functions. We present new methods based on the order-1 vector autoregression (VAR1) for inferring GRNs from gene expression time series. The methods use the automatic relevance determination (ARD) to incorporate the regulatory hub structure into the estimation of VAR1 in a Bayesian framework. Several sparse approximation schemes are applied to the estimated regression weights or VAR1 model to generate the sparse weighted adjacency matrices representing the inferred GRNs. We apply the proposed and several widespread reference methods to infer GRNs with up to 100 genes using simulated, DREAM4 in silico and experimental E. coli gene expression time series. We show that the proposed methods are efficient on simulated hub GRNs and scale-free GRNs using short time series simulated by VAR1s and outperform reference methods on small-scale DREAM4 in silico GRNs and E. coli GRNs. They can utilize the known major regulatory hubs to improve the performance on larger DREAM4 in silico GRNs and E. coli GRNs. The impact of nonlinear time series data on the performance of proposed methods is discussed.

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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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