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 's ROS pathway.
Highlights
Reactive Oxygen Species (ROS) are formed as by-products of normal metabolism of aerobic organisms, they can react with DNA and produce damage [1]
BNc constructed by combining Bayesian networks BN1 and BN2 is equivalent to the Bayesian network learned from the database obtained by merging the two Bayesian networks’ corresponding databases DB1 and DB2 or not, 6 databases were downloaded from the UCI Machine Learning Repository, and the databases of ALARM net and Chest-clinic net were generated by the BN PowerConstructor
We address this question: does the Bayesian network learned from microarray expressions match with a known regulatory pathway?
Summary
Reactive Oxygen Species (ROS) are formed as by-products of normal metabolism of aerobic organisms, they can react with DNA and produce damage [1]. Bayesian network is a Directed Acyclic Graph (DAG) used for representing probabilistic relationships between variables. Large numbers of Bayesian network learning algorithms based on different methodologies have been developed [10,11,12,13] and they have high accuracies in learning Bayesian networks from classic machine learning databases. When applying these algorithms to learn Bayesian networks from microarray data, the accuracies are low. Microarray chip is expensive, it is difficult to obtain a large number of microarray data from one laboratory or one database, and a few hundred expression data can not guarantee a high learning accuracy
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