Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, PR China.
PLoS One. 2013;8(2):e56832. doi: 10.1371/journal.pone.0056832. Epub 2013 Feb 15.
Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the bayesian network from microarray data directly. Although large numbers of bayesian network learning algorithms have been developed, when applying them to learn bayesian networks from microarray data, the accuracies are low due to that the databases they used to learn bayesian networks contain too few microarray data. In this paper, we propose a consensus bayesian network which is constructed by combining bayesian networks from relevant literatures and bayesian networks learned from microarray data. It would have a higher accuracy than the bayesian networks learned from one database. In the experiment, we validated the bayesian network combination algorithm on several classic machine learning databases and used the consensus bayesian network to model the Escherichia coli's ROS pathway.
贝叶斯网络是一种最成功的表示活性氧物种调控途径的图模型。随着微阵列测量数量的增加，直接从微阵列数据构建贝叶斯网络成为可能。尽管已经开发了大量的贝叶斯网络学习算法，但在将它们应用于从微阵列数据中学习贝叶斯网络时，由于它们用于学习贝叶斯网络的数据库包含的微阵列数据太少，因此准确性较低。在本文中，我们提出了一种共识贝叶斯网络，该网络是通过结合相关文献中的贝叶斯网络和从微阵列数据中学习的贝叶斯网络构建的。它将比从一个数据库学习的贝叶斯网络具有更高的准确性。在实验中，我们在几个经典的机器学习数据库上验证了贝叶斯网络组合算法，并使用共识贝叶斯网络对大肠杆菌的 ROS 途径进行建模。
