Learning gene regulatory networks using the bees algorithm

• Published in: Neural computing & applications Vol. 22; no. 1; pp. 63 - 70
• Main Authors: Ruz, Gonzalo A., Goles, Eric
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.01.2013
• Subjects: Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Cont. Dev. of Neural Compt. & Appln; Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Probability and Statistics in Computer Science; Attractors; Swarm intelligence; The bees algorithm; Boolean networks; Simulated annealing
• ISSN: 0941-0643; 1433-3058; 1433-3058
• DOI: 10.1007/s00521-011-0750-z

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.