Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK.
Institute of Computational Biology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, 85764, Germany.
BMC Bioinformatics. 2019 Jan 25;20(1):52. doi: 10.1186/s12859-018-2590-7.
Reverse engineering of gene regulatory networks from time series gene-expression data is a challenging problem, not only because of the vast sets of candidate interactions but also due to the stochastic nature of gene expression. We limit our analysis to nonlinear differential equation based inference methods. In order to avoid the computational cost of large-scale simulations, a two-step Gaussian process interpolation based gradient matching approach has been proposed to solve differential equations approximately.
We apply a gradient matching inference approach to a large number of candidate models, including parametric differential equations or their corresponding non-parametric representations, we evaluate the network inference performance under various settings for different inference objectives. We use model averaging, based on the Bayesian Information Criterion (BIC), to combine the different inferences. The performance of different inference approaches is evaluated using area under the precision-recall curves.
We found that parametric methods can provide comparable, and often improved inference compared to non-parametric methods; the latter, however, require no kinetic information and are computationally more efficient.
从时间序列基因表达数据中反向工程基因调控网络是一个具有挑战性的问题，不仅因为候选相互作用的数量庞大，还因为基因表达的随机性。我们将分析限制在基于非线性微分方程的推理方法上。为了避免大规模模拟的计算成本，已经提出了一种基于两步高斯过程插值的梯度匹配方法来近似求解微分方程。
我们将梯度匹配推理方法应用于大量候选模型，包括参数微分方程或其相应的非参数表示，我们根据不同的推理目标在不同的设置下评估网络推理性能。我们使用基于贝叶斯信息准则（BIC）的模型平均来组合不同的推理。使用精度-召回曲线下的面积来评估不同推理方法的性能。
我们发现参数方法可以提供与非参数方法相当的、甚至更优的推理结果；后者则不需要动力学信息，并且在计算上更有效率。
