School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China.
Sci Rep. 2015 Dec 18;5:17841. doi: 10.1038/srep17841.
Dynamic Bayesian Networks (DBN) have been widely used to recover gene regulatory relationships from time-series data in computational systems biology. Its standard assumption is 'stationarity', and therefore, several research efforts have been recently proposed to relax this restriction. However, those methods suffer from three challenges: long running time, low accuracy and reliance on parameter settings. To address these problems, we propose a novel non-stationary DBN model by extending each hidden node of Hidden Markov Model into a DBN (called HMDBN), which properly handles the underlying time-evolving networks. Correspondingly, an improved structural EM algorithm is proposed to learn the HMDBN. It dramatically reduces searching space, thereby substantially improving computational efficiency. Additionally, we derived a novel generalized Bayesian Information Criterion under the non-stationary assumption (called BWBIC), which can help significantly improve the reconstruction accuracy and largely reduce over-fitting. Moreover, the re-estimation formulas for all parameters of our model are derived, enabling us to avoid reliance on parameter settings. Compared to the state-of-the-art methods, the experimental evaluation of our proposed method on both synthetic and real biological data demonstrates more stably high prediction accuracy and significantly improved computation efficiency, even with no prior knowledge and parameter settings.
动态贝叶斯网络（DBN）已被广泛用于从计算系统生物学中的时间序列数据中恢复基因调控关系。其标准假设是“平稳性”，因此，最近已经提出了一些研究工作来放宽这一限制。然而，这些方法存在三个挑战：运行时间长、准确性低和依赖参数设置。为了解决这些问题，我们通过将隐马尔可夫模型中的每个隐节点扩展为 DBN（称为 HMDBN），提出了一种新颖的非平稳 DBN 模型，该模型可以正确处理基础的时变网络。相应地，提出了一种改进的结构 EM 算法来学习 HMDBN。它大大减少了搜索空间，从而大大提高了计算效率。此外，我们在非平稳假设下推导出了一种新的广义贝叶斯信息准则（称为 BWBIC），它可以显著提高重建准确性，并大大减少过拟合。此外，我们模型的所有参数的重新估计公式都被推导出来，使我们能够避免依赖参数设置。与最先进的方法相比，我们的方法在合成和真实生物数据上的实验评估表明，即使没有先验知识和参数设置，也能更稳定地提高预测准确性，并显著提高计算效率。
