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Learning gene regulatory networks using the bees algorithm

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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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Acknowledgments

The authors would like to thank Conicyt-Chile under grant Fondecyt 3100044 (G.A.R.), Fondecyt 1100003 (E.G.), Basal (Conicyt)-CMM (E.G.), and ANILLO ACT-88 for financially supporting this research.

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Authors and Affiliations

  1. Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Av. Diagonal las Torres, 2640, Peñalolén, Santiago, Chile

    Gonzalo A. Ruz & Eric Goles

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  1. Gonzalo A. Ruz
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  2. Eric Goles
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Correspondence to Gonzalo A. Ruz.

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Ruz, G.A., Goles, E. Learning gene regulatory networks using the bees algorithm. Neural Comput & Applic 22, 63–70 (2013). https://doi.org/10.1007/s00521-011-0750-z

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