Title: Learning the Structure of Bayesian Networks Representing Influence Relations Among Genes
Authors: MASCHERINI Massimiliano
Citation: Proceedings of CIMCA 2008 - International Conference on Computational intelligence for Modelling, Control and Automation p. 1023-1028
Publisher: IEEE Computer Society
Publication Year: 2009
JRC N°: JRC48203
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC48203
DOI: 10.1109/CIMCA.2008.21
Type: Articles in periodicals and books
Abstract: A Bayesian network is a graph-based model of joint multivariate probability distributions that captures properties of conditional independence between variables. Such models are an effective way to characterize probabilistic and causal relations among variables providing a clear methodology for learning from observations. In recent years their use to recover transcriptional regulatory networks from static microarray data is becoming an active area of bioinformatics research. The intent of this paper is to provide a review on structural learning of Bayesian Networks and to compare described methods on a benchmark dataset, the Hepatic Glucose Homeostasis network, that describe results of microarray experiments.
JRC Directorate:Space, Security and Migration

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