A Petri net representation of Bayesian message flows: importance of Bayesian networks for biological applications |
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Authors: | Kurt Lautenbach Alexander Pinl |
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Affiliation: | (1) Institut f?r Softwaretechnik, Universit?t Koblenz-Landau, Universit?tsstr. 1, 56070 Koblenz, Germany |
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Abstract: | This article combines Bayes’ theorem with flows of probabilities, flows of evidences (likelihoods), and fundamental concepts
for learning Bayesian networks as biological models from data. There is a huge amount of biological applications of Bayesian
networks. For example in the fields of protein modeling, pathway modeling, gene expression analysis, DNA sequence analysis,
protein–protein interaction, or protein–DNA interaction. Usually, the Bayesian networks have to be learned (statistically
constructed) from array data. Then they are considered as an executable and analyzable model of the data source. To improve
that, this work introduces a Petri net representation for the propagation of probabilities and likelihoods in Bayesian networks.
The reason for doing so is to exploit the structural and dynamic properties of Petri nets for increasing the transparency
of propagation processes. Consequently the novel Petri nets are called “probability propagation nets”. By means of examples
it is shown that the understanding of the Bayesian propagation algorithm is improved. This is of particular importance for
an exact visualization of biological systems by Bayesian networks. |
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Keywords: | |
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