Learning the Structure of Bayesian Networks Representing Influence Relations
Among Genes
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.
MASCHERINI Massimiliano;
2009-08-13
IEEE Computer Society
JRC48203
https://publications.jrc.ec.europa.eu/repository/handle/JRC48203,
10.1109/CIMCA.2008.21,
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