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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推断具有低阶条件独立性的动态基因调控网络-方法的评估

十多年前，Lèbre（2009）提出了一种推理方法G1DBN，以从高维，稀疏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，它们扩展到动态贝叶斯网络（DBN）。他们提出的结果证明，与相关的套索和收缩法相比，他们的方法具有更好的结构精度，特别是在数据稀疏的情况下，即时间测量的次数n远小于基因p的数目。本文通过仔细的实验​​分析对这些主张提出了挑战，以表明使用G1DBN方法从时序数据进行反向工程的GRN的准确性不如Lèbre（2009）所主张的那样。我们还表明，与G1DBN方法相比，尤其是在数据稀疏时（n << p $ n {<} {<} p $），与从G1DBN方法相比，套索方法对于从模拟数据中学习的图具有更高的结构精度。在鉴定啤酒酵母细胞周期中涉及的转录因子（TFs）方面，Lasso方法也优于G1DBN。