Paper Info
Title
Hybrid multiobjective artificial bee colony with differential evolution applied to motif finding
Abstract
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.
Year
DOI
Venue
2013
10.1007/978-3-642-37189-9_7
EvoBIO
Keywords
Field
DocType
hybrid multiobjective artificial bee,biological significance,differential evolution,proposed algorithm,hybrid multiobjective evolutionary algorithm,dna pattern,de algorithm,multiobjective evolutionary algorithm,important optimization problem,good performance,optimization problem,motif finding,good result
Homo sapiens,Evolutionary algorithm,Computer science,DNA Patterns,Motif (music),Differential evolution,Multi-objective optimization,DNA sequencing,Artificial intelligence,Bioinformatics,Optimization problem
Conference
Citations 
PageRank 
References 
1
0.35
13
Authors
2
Name
Order
Citations
PageRank
David L. González-Álvarez110712.72
Miguel A. Vega-Rodríguez2741113.05