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Neural Computing and Applications
Published in: Neural Computing and Applications 1/2013

01-01-2013 | Cont. Dev. of Neural Compt. & Appln.

Learning gene regulatory networks using the bees algorithm

Authors: Gonzalo A. Ruz, Eric Goles

Published in: Neural Computing and Applications | Issue 1/2013

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Abstract

Learning gene regulatory networks under the threshold Boolean network model is presented. To accomplish this, the swarm intelligence technique called the bees algorithm is formulated to learn networks with predefined attractors. The resulting technique is compared with simulated annealing through simulations. The ability of the networks to preserve the attractors when the updating schemes is changed from parallel to sequential is analyzed as well. Results show that Boolean networks are not very robust when the updating scheme is changed. Robust networks were found only for limit cycle length equal to two and specific network topologies. Throughout the simulations, the bees algorithm outperformed simulated annealing, showing the effectiveness of this swarm intelligence technique for this particular application.
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Metadata
Title
Learning gene regulatory networks using the bees algorithm
Authors
Gonzalo A. Ruz
Eric Goles
Publication date
01-01-2013
Publisher
Springer-Verlag
Published in
Neural Computing and Applications / Issue 1/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-011-0750-z

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