caa a22 4500 445843020 CHVBK 20180317145350.0 cr unu---uuuuu 170323e20110601xx s 000 0 eng 10.1007/s11047-009-9142-z doi (NATIONALLICENCE)springer-10.1007/s11047-009-9142-z A Petri net representation of Bayesian message flows: importance of Bayesian networks for biological applications [Elektronische Daten] [Kurt Lautenbach, Alexander Pinl] 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. Springer Science+Business Media B.V., 2009 Bayesian networks nationallicence Biology nationallicence Petri nets nationallicence Probability nationallicence Propagation nationallicence Lautenbach Kurt Institut für Softwaretechnik, Universität Koblenz-Landau, Universitätsstr. 1, 56070, Koblenz, Germany aut Pinl Alexander Institut für Softwaretechnik, Universität Koblenz-Landau, Universitätsstr. 1, 56070, Koblenz, Germany aut Natural Computing Springer Netherlands 10/2(2011-06-01), 683-709 1567-7818 10:2<683 2011 10 11047 https://doi.org/10.1007/s11047-009-9142-z text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11047-009-9142-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Lautenbach Kurt Institut für Softwaretechnik, Universität Koblenz-Landau, Universitätsstr. 1, 56070, Koblenz, Germany aut NATIONALLICENCE 700 1- Pinl Alexander Institut für Softwaretechnik, Universität Koblenz-Landau, Universitätsstr. 1, 56070, Koblenz, Germany aut NATIONALLICENCE 773 0- Natural Computing Springer Netherlands 10/2(2011-06-01), 683-709 1567-7818 10:2<683 2011 10 11047 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer