SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms.
SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms.
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DOI：
10.1186/1471-2105-7-43
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发表时间：
2006-01-26
影响因子：
3
通讯作者：
Marchal, K
中科院分区：
文献类型：
--
作者：
Van den Bulcke, T;Van Leemput, K;Naudts, B;van Remortel, P;Ma, HW;Verschoren, A;De Moor, B;Marchal, K
基于表达数据的基因调控网络结构推断算法的发展是生物信息学研究的一个重要课题。这些算法的验证需要已知底层网络的基准数据集。由于通常无法获得适当大小和设计的实验数据集，因此显然需要生成表征良好的合成数据集，以便以快速和可重复的方式对学习算法进行彻底测试。在本文中，我们描述了一个网络生成器，创建合成的转录调控网络，并产生模拟的基因表达数据，近似实验数据。通过从先前描述的监管网络中选择子网络来生成网络拓扑。相互作用动力学由基于Michaelis-Menten和Hill动力学的方程建模。我们的研究结果表明，这些拓扑结构的统计特性更接近真正的生物网络比那些不同类型的随机图模型。几个用户可定义的参数调整所得到的数据集相对于结构学习算法的复杂性。这种网络生成技术提供了一个有效的替代现有的方法。生成的网络的拓扑特征更接近于真实的转录网络的特征。网络的模拟可以很好地扩展到大型网络。生成器模拟不同类型的生物相互作用，并产生生物学上合理的合成基因表达数据。
The development of algorithms to infer the structure of gene regulatory networks based on expression data is an important subject in bioinformatics research. Validation of these algorithms requires benchmark data sets for which the underlying network is known. Since experimental data sets of the appropriate size and design are usually not available, there is a clear need to generate well-characterized synthetic data sets that allow thorough testing of learning algorithms in a fast and reproducible manner. In this paper we describe a network generator that creates synthetic transcriptional regulatory networks and produces simulated gene expression data that approximates experimental data. Network topologies are generated by selecting subnetworks from previously described regulatory networks. Interaction kinetics are modeled by equations based on Michaelis-Menten and Hill kinetics. Our results show that the statistical properties of these topologies more closely approximate those of genuine biological networks than do those of different types of random graph models. Several user-definable parameters adjust the complexity of the resulting data set with respect to the structure learning algorithms. This network generation technique offers a valid alternative to existing methods. The topological characteristics of the generated networks more closely resemble the characteristics of real transcriptional networks. Simulation of the network scales well to large networks. The generator models different types of biological interactions and produces biologically plausible synthetic gene expression data.
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影响因子：
3
作者：
Zhu D;Qin ZS
通讯作者：
Qin ZS
影响因子：
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作者：
Husmeier, D
通讯作者：
Husmeier, D
影响因子：
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作者：
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通讯作者：
Ye, Keying
影响因子：
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作者：
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通讯作者：
Alon, U
影响因子：
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作者：
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通讯作者：
Miyano, Satoru