Anzeige
Erschienen in:
2013 | OriginalPaper | Buchkapitel
Hybrid Multiobjective Artificial Bee Colony with Differential Evolution Applied to Motif Finding
verfasst von : David L. González-Álvarez, Miguel A. Vega-Rodríguez
Erschienen in: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.