Identifying gene regulatory networks from experimental data
Identifying gene regulatory networks from experimental data
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DOI：
10.1016/s0167-8191(00)00092-2
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发表时间：
2001-01-01
影响因子：
1.4
通讯作者：
Skiena, SS
中科院分区：
文献类型：
--
作者：
Chen, T;Filkov, V;Skiena, SS
本文研究了一个基因调控网络模型，其中一个基因被其他基因激活或抑制。为了识别这些网络的复杂结构，我们提出了一种分析表达分析中出现的大型、多个时间序列数据集的方法，并通过理论和案例研究对其进行评估。我们首先构建一个图表，表示所有基因对之间所有假定的激活/抑制关系，然后通过解决组合优化问题来修剪该图表，以识别一小组有趣的候选调控元件。我们实现了这个方法并将其应用到真实的数据集中。对于这个特定的模型，我们提出了最大基因调控问题的几种算法和复杂性结果，以识别调控所有基因的最小基因集。 (C) 2001 Elsevier Science B.V. 保留所有权利。
This paper studies a gene regulatory network model where a gene is activated or inhibited by other genes. To identify the complicated structure of these networks, we propose a methodology for analyzing large, multiple time-series data sets arising in expression analysis, and evaluate it both theoretically and through a case study. We first build a graph representing all putative activation/inhibition relationships between all pairs of genes, and then prune this graph by solving a combinatorial optimization problem to identify a small set of interesting candidate regulatory elements. We implemented this method and applied it into a real data set. For this particular model, we present several algorithmic and complexity results for the maximum gene regulation problem to identify the smallest set of genes that regulate all genes. (C) 2001 Elsevier Science B.V. All rights reserved.