Comparing multiobjective artificial bee colony adaptations for discovering DNA motifs

Authors:
David L. González-Álvarez;Miguel A. Vega-Rodríguez;Juan A. Gómez-Pulido;Juan M. Sánchez-Pérez
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;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'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Year:
2012

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Abstract

Multiobjective optimization is successfully applied in many biological problems. Currently, most biological problems require to optimize more than one single objective at the same time, resulting in Multiobjective Optimization Problems (MOP). In the last years, multiple metaheuristics have been successfully used to solve optimization problems. However, many of them are designed to solve problems with only one objective function. In this work, we study several multiobjective adaptations to solve one of the most important biological problems, the Motif Discovery Problem (MDP). MDP aims to discover novel Transcription Factor Binding Sites (TFBS) in DNA sequences, maximizing three conflicting objectives: motif length, support, and similarity. For this purpose, we have used the Artificial Bee Colony algorithm, a novel Swarm Intelligence algorithm based on the intelligent behavior of honey bees. As we will see, the use of one or another multiobjective adaptation causes significant differences in the results.