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cated by general medical knowledge, have been iden-
tified as missing. For example, a connection between
Magnesium (Mg) and HDL Cholesterol. From a med-
ical point of view, the NAFLD graph generated by the
Physarum Learner correctly reflects current medical
knowledge and shows great potential for future appli-
cations and further refinement. The behaviour of the
Physarum Learner is in line with previous experience
showing that it tends to learn the stronger, hence more
important, connections of the network.
5 CONCLUSIONS
A novel structure learning algorithm for Bayesian
Networks has been introduced that initializes a fully
connected network and uses the Physarum Solver
to delete edges between nodes which are below a
given correlation threshold. The Physarum Learner
was compared to the LAGD Hill Climber where the
Physarum Learner could learn competitive network
structures for the three real networks, but performed
worse than LAGD with respect to the learned BDeu
and MDL score. It was shown, that it is generally
possible to learn the structure of a Bayesian Network
with the Physarum Solver. The Physarum Learner
shows strongly growing execution time for networks
with many nodes. This is in part because the algo-
rithm has not yet been optimized for speed and mem-
ory usage. However, for networks with a small num-
ber of nodes and a large number of data points, the
Physarum Learner is clearly more time efficient than
LAGD and probably other score based heuristic meth-
ods.
ACKNOWLEDGEMENTS
This work was supported by BioPersMed (COMET
K-project 825329), which is funded by the Federal
Ministry of Transport, Innovation and Technology
(BMVIT) and the Federal Ministry of Economics
and Labour/the Federal Ministry of Economy, Family
and Youth (BMWA/BMWFJ) and the Styrian Busi-
ness Promotion Agency (SFG). Valuable discussions
with E. Wichro, N. Lanner, R. Charchoghlyan and K.
Sargsyan are much appreciated.
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