DREAM3: network inference using dynamic context likelihood of relatedness and the inferelator.
DREAM3: network inference using dynamic context likelihood of relatedness and the inferelator.
复制标题
DOI：
10.1371/journal.pone.0009803
复制
发表时间：
2010-03-22
期刊：
影响因子：
3.7
通讯作者：
Bonneau R
中科院分区：
文献类型：
--
作者：
Madar A;Greenfield A;Vanden-Eijnden E;Bonneau R
目前，许多旨在从系统生物学数据中学习调控网络的工作必须平衡模型的复杂性与数据的可用性和质量。基于无单元度量（如互信息）学习监管关联的方法很有吸引力，因为它们可以很好地扩展，并将每次交互的自由参数（模型复杂性）数量降至最低。相比之下，基于显式动力学模型学习调节网络的方法更加复杂，规模也不太灵活，但很有吸引力，因为它们可以直接预测转录动态并解决许多调节相互作用的方向性。我们的目标是调查是否可扩展的信息为基础的方法（如上下文相关性的方法）和更明确的动力学模型（如Inferelator 1.0）证明协同时相结合。我们测试了一个管道，首先使用一种新的修改的上下文相关性（混合的，修改为使用时间序列数据）来定义可能的监管相互作用，然后Inferelator 1.0用于最终的模型选择，并建立一个明确的动态模型。我们的方法在DREAM 3 100-gene in silico networks挑战中排名第二。Mixed-Doppler和Inferelator 1.0是互补的，相对于任何单一的测试方法，表现出很大的性能增益，在低召回率值下的精确度特别高。将提供的数据集分成四组（敲除、敲除、时间序列和组合）揭示了单独使用全面敲除数据提供了最佳性能。Inferelator 1.0在解决监管互动的方向性方面特别强大，即“谁监管谁”（大约有10%的确定的真阳性被正确解决）。高入度基因的性能下降，即随着每个靶基因的调节器数量的增加，但不与出度，即性能不受调节中心的存在。
Many current works aiming to learn regulatory networks from systems biology data must balance model complexity with respect to data availability and quality. Methods that learn regulatory associations based on unit-less metrics, such as Mutual Information, are attractive in that they scale well and reduce the number of free parameters (model complexity) per interaction to a minimum. In contrast, methods for learning regulatory networks based on explicit dynamical models are more complex and scale less gracefully, but are attractive as they may allow direct prediction of transcriptional dynamics and resolve the directionality of many regulatory interactions. We aim to investigate whether scalable information based methods (like the Context Likelihood of Relatedness method) and more explicit dynamical models (like Inferelator 1.0) prove synergistic when combined. We test a pipeline where a novel modification of the Context Likelihood of Relatedness (mixed-CLR, modified to use time series data) is first used to define likely regulatory interactions and then Inferelator 1.0 is used for final model selection and to build an explicit dynamical model. Our method ranked 2nd out of 22 in the DREAM3 100-gene in silico networks challenge. Mixed-CLR and Inferelator 1.0 are complementary, demonstrating a large performance gain relative to any single tested method, with precision being especially high at low recall values. Partitioning the provided data set into four groups (knock-down, knock-out, time-series, and combined) revealed that using comprehensive knock-out data alone provides optimal performance. Inferelator 1.0 proved particularly powerful at resolving the directionality of regulatory interactions, i.e. “who regulates who” (approximately of identified true positives were correctly resolved). Performance drops for high in-degree genes, i.e. as the number of regulators per target gene increases, but not with out-degree, i.e. performance is not affected by the presence of regulatory hubs.
登录
查看更多内容
影响因子：
64.5
作者：
Bonneau, Richard;Facciotti, Marc T.;Baliga, Nitin S.
通讯作者：
Baliga, Nitin S.
影响因子：
3
作者：
Chang WC;Li CW;Chen BS
通讯作者：
Chen BS
DOI：
10.1073/pnas.220392197
发表时间：
2000-10-24
影响因子：
11.1
作者：
Butte, AJ;Tamayo, P;Kohane, IS
通讯作者：
Kohane, IS
DOI：
10.1073/pnas.112318199
发表时间：
2002-07-23
影响因子：
11.1
作者：
Bernstein, JA;Khodursky, AB;Cohen, SN
通讯作者：
Cohen, SN
影响因子：
9.9
作者：
通讯作者：
--