Abstract
Particle Swarm Optimization (PSO) has been successfully applied to a wide range of fields. The recent introduction of quantum mechanics principles into PSO has given rise to a Quantum behaviour PSO (QPSO) algorithm. This paper investigates its application into motif discovery, a challenging task in bioinformatics and molecular biology. Given a set of input DNA sequences, the proposed framework acts as a search process where a population of particles is depicted by a quantum behavior. Each particle represents a set of regulatory patterns from which a consensus pattern or motif model is derived. The corresponding fitness function is related to the total number of pairwise matches between nucleotides in the input sequences. Experiment results on synthetic and real data are very promising and prove the effectiveness of the proposed framework.
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Meshoul, S., Al-Owaisheq, T. (2009). QPSO-MD: A Quantum Behaved Particle Swarm Optimization for Consensus Pattern Identification. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2009. Communications in Computer and Information Science, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04962-0_42
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DOI: https://doi.org/10.1007/978-3-642-04962-0_42
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