An integrative approach to infer regulation programs in a transcription regulatory module network.
An integrative approach to infer regulation programs in a transcription regulatory module network.
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DOI：
10.1155/2012/245968
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发表时间：
2012
影响因子：
--
通讯作者：
Butler G
中科院分区：
文献类型：
--
作者：
Qi J;Michoel T;Butler G
模块网络方法是一种特殊类型的贝叶斯网络算法，已被提出来从基因表达数据推断转录调控网络。在该方法中，一个模块代表一组基因，它们具有相似的表达谱并受相同的转录因子调控。学习模块网络的过程包括两个步骤：首先将基因聚类成模块，然后推断每个模块的调控程序（转录因子）。许多算法被设计来推断给定基因模块的调控程序，并且这些算法在检测调控关系方面表现出截然不同的偏差。在这项工作中，我们探索了整合不同算法结果的可能性。我们选择的整合方法有并集、交集和加权排名聚合。酵母数据集中的实验表明，并集和加权排名聚合方法产生的预测比单独算法给出的预测更准确，而交集方法在预测准确性方面没有任何改进。此外，有些令人惊讶的是，并集方法的计算成本比排名聚合低，但获得了与排名聚合给出的可比较的结果。
The module network method, a special type of Bayesian network algorithms, has been proposed to infer transcription regulatory networks from gene expression data. In this method, a module represents a set of genes, which have similar expression profiles and are regulated by same transcription factors. The process of learning module networks consists of two steps: first clustering genes into modules and then inferring the regulation program (transcription factors) of each module. Many algorithms have been designed to infer the regulation program of a given gene module, and these algorithms show very different biases in detecting regulatory relationships. In this work, we explore the possibility of integrating results from different algorithms. The integration methods we select are union, intersection, and weighted rank aggregation. Experiments in a yeast dataset show that the union and weighted rank aggregation methods produce more accurate predictions than those given by individual algorithms, whereas the intersection method does not yield any improvement in the accuracy of predictions. In addition, somewhat surprisingly, the union method, which has a lower computational cost than rank aggregation, achieves comparable results as given by rank aggregation.
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DOI：
10.1093/bioinformatics/btq395
发表时间：
2010-09-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Bonnet E;Michoel T;Van de Peer Y
通讯作者：
Van de Peer Y
影响因子：
5.8
作者：
Joshi, Anagha;De Smet, Riet;Michoel, Tom
通讯作者：
Michoel, Tom
影响因子：
5.8
作者：
Pihur, Vasyl;Datta, Susmita;Datta, Somnath
通讯作者：
Datta, Somnath
影响因子：
3.7
作者：
Prill RJ;Marbach D;Saez-Rodriguez J;Sorger PK;Alexopoulos LG;Xue X;Clarke ND;Altan-Bonnet G;Stolovitzky G
通讯作者：
Stolovitzky G
DOI：
10.1111/j.2517-6161.1977.tb01600.x
发表时间：
1977-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL
影响因子：
--
作者：
DEMPSTER, AP;LAIRD, NM;RUBIN, DB
通讯作者：
RUBIN, DB