Applying a multiobjective gravitational search algorithm (MO-GSA) to discover motifs

  • Authors:

  • David L. González-Álvarez;Miguel A. Vega-Rodríguez;Juan A. Gómez-Pulido;Juan M. Sánchez-Pérez

  • Affiliations:

  • University of Extremadura, Dep. of Technologies of Computers and Communications, Cáceres, Spain;University of Extremadura, Dep. of Technologies of Computers and Communications, Cáceres, Spain;University of Extremadura, Dep. of Technologies of Computers and Communications, Cáceres, Spain;University of Extremadura, Dep. of Technologies of Computers and Communications, Cáceres, Spain

  • Venue:
  • IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
  • Year:
  • 2011

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Abstract

Currently there are a large number of Bioinformatics problems that are tackled using computational techniques. The problems discussed range from small molecules to complex systems where many organisms coexist. Among all these issues, we can highlight genomics: it studies the genomes of microorganisms, plants and animals. To discover common patterns, motifs, in a set of deoxyribonucleic acid (DNA) sequences is one of the important sequence analysis problems and it is known as Motif Discovery Problem (MDP). In this work we propose the use of computational Swarm Intelligence for solving the MDP. A new heuristic based on the law of gravity and the notion of mass interactions, the Gravitational Search Algorithm (GSA), is chosen for this purpose, but adapted to a multiobjective context (MO-GSA). To test the performance of the MO-GSA, we have used twelve real data sets corresponding to alive beings. After performing several comparisons with other approaches published in the literature, we conclude that this algorithm outperforms the results obtained by others.