推断具有低阶条件独立性的动态基因调控网络-对该方法的评价

IF 0.9 4区 数学 Q3 Mathematics
Hamda Ajmal, M. G. Madden
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引用次数: 1

摘要

摘要十多年前,Lèbre(2009)提出了一种推理方法G1DBN,用于从高维、稀疏的时间序列基因表达数据中学习基因调控网络(GRN)的结构。他们的方法基于低阶条件独立图的概念,并将其扩展到动态贝叶斯网络(DBN)。他们提出的结果表明,与相关的拉索和收缩方法相比,他们的方法产生了更好的结构精度,特别是在数据稀疏的情况下,即时间测量的数量n远小于基因的数量p。本文通过仔细的实验分析对这些说法提出了质疑,以表明使用G1DBN方法从时间序列数据中反向工程的GRN不如Lèbre(2009)所声称的准确。我们还表明,与G1DBN方法相比,Lasso方法对从模拟数据中学习的图产生了更高的结构精度,特别是当数据稀疏时(n<本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Inferring dynamic gene regulatory networks with low-order conditional independencies – an evaluation of the method
Abstract Over a decade ago, Lèbre (2009) proposed an inference method, G1DBN, to learn the structure of gene regulatory networks (GRNs) from high dimensional, sparse time-series gene expression data. Their approach is based on concept of low-order conditional independence graphs that they extend to dynamic Bayesian networks (DBNs). They present results to demonstrate that their method yields better structural accuracy compared to the related Lasso and Shrinkage methods, particularly where the data is sparse, that is, the number of time measurements n is much smaller than the number of genes p. This paper challenges these claims using a careful experimental analysis, to show that the GRNs reverse engineered from time-series data using the G1DBN approach are less accurate than claimed by Lèbre (2009). We also show that the Lasso method yields higher structural accuracy for graphs learned from the simulated data, compared to the G1DBN method, particularly when the data is sparse ( n < < p $n{< }{< }p$ ). The Lasso method is also better than G1DBN at identifying the transcription factors (TFs) involved in the cell cycle of Saccharomyces cerevisiae.
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来源期刊
Statistical Applications in Genetics and Molecular Biology
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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