Boolean networks using the chi-square test for inferring large-scale gene regulatory networks.
Boolean networks using the chi-square test for inferring large-scale gene regulatory networks.
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DOI：
10.1186/1471-2105-8-37
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发表时间：
2007-02-01
影响因子：
3
通讯作者：
Park T
中科院分区：
文献类型：
--
作者：
Kim H;Lee JK;Park T
布尔网络（BN）建模是从时间序列微阵列数据构建基因调控网络的常用方法。然而，它的主要缺点是，它的计算时间是非常高的或往往是不切实际的构建大规模的基因网络。我们提出了一种变量选择方法，不仅大大减少了BN的计算时间，但也获得了最佳的网络结构，通过使用卡方统计检验列联表中的独立性。在模拟和真实的酵母细胞周期微阵列基因表达数据集上，将该方法与原始BN方法的计算时间和网络结构估计精度进行了比较。我们的研究结果表明，所提出的卡方检验（CST）为基础的BN方法显着提高了计算时间，而其识别所有真正的网络机制的能力是有效的全搜索BN方法相同。当最佳拟合扩展问题的误差大小为0和1时，所提出的BN算法比原始BN算法分别快约70.8和7.6倍。此外，所提出的基于CST的BN算法的假阳性错误率往往小于原始BN的假阳性错误率。基于CST的BN方法大大提高了原始BN算法的计算时间。因此，它可以有效地推断大规模的基因调控网络机制。
Boolean network (BN) modeling is a commonly used method for constructing gene regulatory networks from time series microarray data. However, its major drawback is that its computation time is very high or often impractical to construct large-scale gene networks. We propose a variable selection method that are not only reduces BN computation times significantly but also obtains optimal network constructions by using chi-square statistics for testing the independence in contingency tables. Both the computation time and accuracy of the network structures estimated by the proposed method are compared with those of the original BN methods on simulated and real yeast cell cycle microarray gene expression data sets. Our results reveal that the proposed chi-square testing (CST)-based BN method significantly improves the computation time, while its ability to identify all the true network mechanisms was effectively the same as that of full-search BN methods. The proposed BN algorithm is approximately 70.8 and 7.6 times faster than the original BN algorithm when the error sizes of the Best-Fit Extension problem are 0 and 1, respectively. Further, the false positive error rate of the proposed CST-based BN algorithm tends to be less than that of the original BN. The CST-based BN method dramatically improves the computation time of the original BN algorithm. Therefore, it can efficiently infer large-scale gene regulatory network mechanisms.
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影响因子：
5.8
作者：
Perrin, Bruno-Edouard;Ralaivola, Liva;d'Alche-Buc, Florence
通讯作者：
d'Alche-Buc, Florence
影响因子：
5.8
作者：
Shmulevich, I;Dougherty, ER;Zhang, W
通讯作者：
Zhang, W
影响因子：
--
作者：
Shmulevich, Ilya;Gluhovsky, Ilya;Hashimoto, Ronaldo F;Dougherty, Edward R;Zhang, Wei
通讯作者：
Zhang, Wei
影响因子：
64.8
作者：
Jeong, H;Tombor, B;Barabási, AL
通讯作者：
Barabási, AL
影响因子：
5.8
作者：
Shmulevich, I;Zhang, W
通讯作者：
Zhang, W