Larjo Antti, Lähdesmäki Harri
Department of Information and Computer Science, Aalto University, FI-00076Aalto, Finland.
Department of Signal Processing, Tampere University of Technology, Tampere, FI-33101 Finland.
EURASIP J Bioinform Syst Biol. 2015 Jun 20;2015:6. doi: 10.1186/s13637-015-0024-7. eCollection 2015 Dec.
Bayesian networks have become popular for modeling probabilistic relationships between entities. As their structure can also be given a causal interpretation about the studied system, they can be used to learn, for example, regulatory relationships of genes or proteins in biological networks and pathways. Inference of the Bayesian network structure is complicated by the size of the model structure space, necessitating the use of optimization methods or sampling techniques, such Markov Chain Monte Carlo (MCMC) methods. However, convergence of MCMC chains is in many cases slow and can become even a harder issue as the dataset size grows. We show here how to improve convergence in the Bayesian network structure space by using an adjustable proposal distribution with the possibility to propose a wide range of steps in the structure space, and demonstrate improved network structure inference by analyzing phosphoprotein data from the human primary T cell signaling network.
贝叶斯网络已成为用于对实体之间概率关系进行建模的流行方法。由于其结构也可以对所研究的系统给出因果解释，因此它们可用于学习例如生物网络和通路中基因或蛋白质的调控关系。贝叶斯网络结构的推断因模型结构空间的大小而变得复杂，这就需要使用优化方法或采样技术，如马尔可夫链蒙特卡罗（MCMC）方法。然而，在许多情况下，MCMC链的收敛速度很慢，并且随着数据集规模的增长，这可能会成为一个更棘手的问题。我们在此展示了如何通过使用可调提议分布来改善贝叶斯网络结构空间中的收敛，该提议分布能够在结构空间中提出广泛的步骤范围，并通过分析来自人类原代T细胞信号网络的磷酸化蛋白数据来证明改进的网络结构推断。
