Hybrid multiobjective artificial bee colony with differential evolution applied to motif finding

Authors:
David L. González-Álvarez;Miguel A. Vega-Rodríguez
Affiliations:
Department of Technologies of Computers and Communications, ARCO Research Group, Escuela Politécnica, University of Extremadura, Cáceres, Spain;Department of Technologies of Computers and Communications, ARCO Research Group, Escuela Politécnica, University of Extremadura, Cáceres, Spain
Venue:
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Year:
2013

Citing 12
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Abstract

The Multiobjective Artificial Bee Colony with Differential Evolution (MO-ABC/DE) is a new hybrid multiobjective evolutionary algorithm proposed for solving optimization problems. One important optimization problem in Bioinformatics is the Motif Discovery Problem (MDP), applied to the specific task of discovering DNA patterns (motifs) with biological significance, such as DNA-protein binding sites, replication origins or transcriptional DNA sequences. In this work, we apply the MO-ABC/DE algorithm for solving the MDP using as benchmark genomic data belonging to four organisms: drosophila melanogaster, homo sapiens, mus musculus, and saccharomyces cerevisiae. To demonstrate the good performance of our algorithm we have compared its results with those obtained by four multiobjective evolutionary algorithms, and their predictions with those made by thirteen well-known biological tools. As we will see, the proposed algorithm achieves good results from both computer science and biology point of views.