Swarm intelligence for optimizing the parameters of multiple sequence aligners

Detalhes bibliográficos
Autor(a) principal: Rubio-Largo, Álvaro
Data de Publicação: 2018
Outros Autores: Vanneschi, Leonardo, Castelli, Mauro, Vega-Rodríguez, Miguel A.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/151420
Resumo: Rubio-Largo, Á., Vanneschi, L., Castelli, M., & Vega-Rodríguez, M. A. (2018). Swarm intelligence for optimizing the parameters of multiple sequence aligners. Swarm and Evolutionary Computation. DOI: 10.1016/j.swevo.2018.04.003
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spelling Swarm intelligence for optimizing the parameters of multiple sequence alignersCharacteristics-based frameworkEvolutionary algorithmsMultiple sequence alignmentSwarm intelligenceComputer Science(all)Mathematics(all)SDG 3 - Good Health and Well-beingRubio-Largo, Á., Vanneschi, L., Castelli, M., & Vega-Rodríguez, M. A. (2018). Swarm intelligence for optimizing the parameters of multiple sequence aligners. Swarm and Evolutionary Computation. DOI: 10.1016/j.swevo.2018.04.003Different aligner heuristics can be found in the literature to solve the Multiple Sequence Alignment problem. These aligners rely on the parameter configuration proposed by their authors (also known as default parameter configuration), that tried to obtain good results (alignments with high accuracy and conservation) for any input set of unaligned sequences. However, the default parameter configuration is not always the best parameter configuration for every input set; namely, depending on the biological characteristics of the input set, one may be able to find a better parameter configuration that outputs a more accurate and conservative alignment. This work's main contributions include: to study the input set's biological characteristics and to then apply the best parameter configuration found depending on those characteristics. The framework uses a pre-computed file to take the best parameter configuration found for a dataset with similar biological characteristics. In order to create this file, we use a Particle Swarm Optimization (PSO) algorithm, that is, an algorithm based on swarm intelligence. To test the effectiveness of the characteristic-based framework, we employ five well-known aligners: Clustal W, DIALIGN-TX, Kalign2, MAFFT, and MUSCLE. The results of these aligners see clear improvements when using the proposed characteristic-based framework.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNRubio-Largo, ÁlvaroVanneschi, LeonardoCastelli, MauroVega-Rodríguez, Miguel A.2024-01-27T01:32:02Z2018-10-012018-10-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/151420eng2210-6502PURE: 4168066https://doi.org/10.1016/j.swevo.2018.04.003info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:33:52Zoai:run.unl.pt:10362/151420Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:35.158964Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Swarm intelligence for optimizing the parameters of multiple sequence aligners
title Swarm intelligence for optimizing the parameters of multiple sequence aligners
spellingShingle Swarm intelligence for optimizing the parameters of multiple sequence aligners
Rubio-Largo, Álvaro
Characteristics-based framework
Evolutionary algorithms
Multiple sequence alignment
Swarm intelligence
Computer Science(all)
Mathematics(all)
SDG 3 - Good Health and Well-being
title_short Swarm intelligence for optimizing the parameters of multiple sequence aligners
title_full Swarm intelligence for optimizing the parameters of multiple sequence aligners
title_fullStr Swarm intelligence for optimizing the parameters of multiple sequence aligners
title_full_unstemmed Swarm intelligence for optimizing the parameters of multiple sequence aligners
title_sort Swarm intelligence for optimizing the parameters of multiple sequence aligners
author Rubio-Largo, Álvaro
author_facet Rubio-Largo, Álvaro
Vanneschi, Leonardo
Castelli, Mauro
Vega-Rodríguez, Miguel A.
author_role author
author2 Vanneschi, Leonardo
Castelli, Mauro
Vega-Rodríguez, Miguel A.
author2_role author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Rubio-Largo, Álvaro
Vanneschi, Leonardo
Castelli, Mauro
Vega-Rodríguez, Miguel A.
dc.subject.por.fl_str_mv Characteristics-based framework
Evolutionary algorithms
Multiple sequence alignment
Swarm intelligence
Computer Science(all)
Mathematics(all)
SDG 3 - Good Health and Well-being
topic Characteristics-based framework
Evolutionary algorithms
Multiple sequence alignment
Swarm intelligence
Computer Science(all)
Mathematics(all)
SDG 3 - Good Health and Well-being
description Rubio-Largo, Á., Vanneschi, L., Castelli, M., & Vega-Rodríguez, M. A. (2018). Swarm intelligence for optimizing the parameters of multiple sequence aligners. Swarm and Evolutionary Computation. DOI: 10.1016/j.swevo.2018.04.003
publishDate 2018
dc.date.none.fl_str_mv 2018-10-01
2018-10-01T00:00:00Z
2024-01-27T01:32:02Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/151420
url http://hdl.handle.net/10362/151420
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2210-6502
PURE: 4168066
https://doi.org/10.1016/j.swevo.2018.04.003
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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