Abstract
In this work we propose the application of a Swarm Intelligence (SI) algorithm to solve the Motif Discovery Problem (MDP), applied to the specific task of discovering novel Transcription Factor Binding Sites (TFBS) in DNA sequences. In the last years there have appeared many new evolutionary algorithms based on the collective intelligence. Finding TFBS is crucial for understanding the gene regulatory relationship but, motifs are weakly conserved, and motif discovery is an NP-hard problem. Therefore, the use of such algorithms can be a good way to obtain quality results. The chosen algorithm is the Artificial Bee Colony (ABC), it is an optimization algorithm based on the intelligent foraging behaviour of honey bee swarm. To solve the MDP we have applied multiobjective optimization and consequently, we have adapted the ABC to multiobjective problems, defining the Multiobjective Artificial Bee Colony (MOABC) algorithm. New results have been obtained, that significantly improve those published in previous researches.
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González-Álvarez, D.L., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M. (2011). Finding Motifs in DNA Sequences Applying a Multiobjective Artificial Bee Colony (MOABC) Algorithm. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2011. Lecture Notes in Computer Science, vol 6623. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20389-3_9
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DOI: https://doi.org/10.1007/978-3-642-20389-3_9
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