title: Using consensus bayesian network to model the reactive oxygen species regulatory pathway.
abstract: 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.
authors: Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, PR China.
authors: Liangdong
authors: L
authors: Hu
authors: Limin
authors: L
authors: Wang
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D004926:Escherichia coli / Q000378:metabolism; D008954:Models, Biological; D017382:Reactive Oxygen Species / Q000378:metabolism*
chemical_list: D017382:Reactive Oxygen Species