Multiobjective optimization algorithms for motif discovery in DNA sequences

Optimization techniques have become powerful tools for approaching multiple NP-hard optimization problems. In this kind of problem it is practically impossible to obtain optimal solutions, thus we must apply approximation strategies such as metaheuristics. In this paper, seven metaheuristics have be...

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Vydáno v:Genetic programming and evolvable machines Ročník 16; číslo 2; s. 167 - 209
Hlavní autoři: González-Álvarez, David L., Vega-Rodríguez, Miguel A., Rubio-Largo, Álvaro
Médium: Journal Article
Jazyk:angličtina
Vydáno: Boston Springer US 01.06.2015
Témata:
Artificial Intelligence
Biomedical Engineering and Bioengineering
Compilers
Computer Science
Electrical Engineering
Interpreters
Programming Languages
Programming Techniques
Software Engineering/Programming and Operating Systems
Computer science
Metaheuristics
Bioinformatics
Multiobjective optimization
Motif discovery
ISSN:1389-2576, 1573-7632
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Shrnutí:Optimization techniques have become powerful tools for approaching multiple NP-hard optimization problems. In this kind of problem it is practically impossible to obtain optimal solutions, thus we must apply approximation strategies such as metaheuristics. In this paper, seven metaheuristics have been used to address an important biological problem known as the motif discovery problem. As it is defined as a multiobjective optimization problem, we have adapted the proposed algorithms to this optimization context. We evaluate the proposed metaheuristics on 54 sequence datasets that belong to four organisms with different numbers of sequences and sizes. The results have been analysed in order to discover which algorithm performs best in each case. The algorithms implemented and the results achieved can assist biological researchers in the complicated task of finding DNA patterns with an important biological relevance.
ISSN:1389-2576
1573-7632
DOI:10.1007/s10710-014-9232-2