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dc.contributor.authorGonzález Sánchez, Belén-
dc.contributor.authorVega Rodríguez, Miguel Ángel-
dc.contributor.authorSantander Jiménez, Sergio-
dc.descriptionPublicado en: Expert Systems With Applications (Volume: 136, December 2019, pp. 83-93).
dc.description.abstractAn important goal in synthetic biology is to maximize the expression levels of proteins. For this purpose, multiple genes encoding the same protein can be integrated into the host genome. However, this approach is affected by two key issues. Firstly, codons with better adaptation indexes should be used, since some synonymous codons are better adapted than others. Secondly, the multiple protein-coding sequences should be as different as possible to avoid the loss of gene copies due to homologous recombination. Therefore, this task shows strict biological requirements that make it difficult to tackle. In this work, we design and implement a computational intelligence approach to address this problem, the Multi-Objective Shuffled Frog Leaping Algorithm (MOSFLA). This method combines the optimization capabilities provided by parallel searches, multiple operators, and memetic strategies to tackle problems with difficult solution quality requirements. Several alternatives have been comparatively analyzed, including MOSFLA variants with three objectives as in other approaches from the literature and also variants with only two objectives. Experiments on nine real-world protein datasets give account of the improved, statistically significant performance achieved over the related work, attending to different quality metrics, confirming that our proposal satisfactorily deals with the complex nature of the problem.es_ES
dc.description.sponsorship- Agencia Estatal de Investigación y Fondos FEDER. Ayuda TIN2016-76259-P - Fundação para a Ciência e a Tecnologia. Ayudas UID/CEC/50021/2019 y SFRH/BPD/119220/2016 - Junta de Extremadura y Fondos FEDER. Ayudas GR18090 y IB16002es_ES
dc.format.extent40 p.es_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectAlgoritmo metaheurístico memético multiobjetivoes_ES
dc.subjectMulti-objective memetic meta-heuristic algorithmes_ES
dc.subjectDiseño de múltiples geneses_ES
dc.subjectDesign of multiple geneses_ES
dc.subjectCodificación de la misma proteínaes_ES
dc.subjectEncoding of the same proteines_ES
dc.subjectOptimización multiobjetivoes_ES
dc.subjectMulti-objective optimizationes_ES
dc.subjectSecuencia codificadora de proteínas (CDS)es_ES
dc.subjectProtein-coding sequence (CDS)es_ES
dc.titleMulti-objective memetic meta-heuristic algorithm for encoding the same protein with multiple geneses_ES
dc.subject.unesco1203.17 Informáticaes_ES
dc.subject.unesco2410.07 Genética Humanaes_ES
dc.subject.unesco1203.04 Inteligencia Artificiales_ES
europeana.dataProviderUniversidad de Extremadura. Españaes_ES
dc.identifier.bibliographicCitationBelen Gonzalez-Sanchez, Miguel A. Vega-Rodr«õguez, Sergio Santander-Jim«enez, Multi-objective Memetic Meta-heuristic Algorithm for Encoding the Same Protein with Multiple Genes, Expert Systems With Applications (2019), doi:
dc.contributor.affiliationUniversidad de Extremadura. Departamento de Tecnología de los Computadores y de las Comunicacioneses_ES
dc.contributor.affiliationUniversidade de Lisboa. Portugal-
dc.identifier.publicationtitleExpert Systems With Applicationses_ES
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