A Hybrid Genetic Algorithm using Progressive Alignment and Consistency based Approach for Multiple Sequence Alignments

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dc.contributor.authorGomes, Vitoria Zanon [UNESP]
dc.contributor.authorAndrade, Matheus Carreira [UNESP]
dc.contributor.authorAmorim, Anderson Rici [UNESP]
dc.contributor.authorZafalon, Geraldo Francisco Donega [UNESP]
dc.contributor.authorFilipe, J.
dc.contributor.authorSmialek, M.
dc.contributor.authorBrodsky, A.
dc.contributor.authorHammoudi, S.
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniv Paulista
dc.date.accessioned2022-11-30T13:44:18Z
dc.date.available2022-11-30T13:44:18Z
dc.date.issued2022-01-01
dc.description.abstractThe multiple sequence alignment is one of the most important tasks in bioinformatics, since it allows to analyze multiple sequences at the same time. There are many approaches for this problem such as heuristics and metaheuristics, that generally lead to great results in a plausible time, being among the most used approaches. The genetic algorithm is one of the most used methods because of its results quality, but it had a problematic disadvantage: it can be easily trapped in a local optima result, not being able to reach better alignments. In this work we propose a hybrid genetic algorithm with progressive and consistency-based methods as a way to smooth the local optima problem and improve the quality of the alignments. The obtained results show that our method was able to improve the quality of AG results 2 a 27 times, smoothing the local maximum problem and providing results with more biological significance.en
dc.description.affiliationUniv Estadual Paulista UNESP, Dept Comp Sci & Stat, Rua Cristovelo Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP, Brazil
dc.description.affiliationUniv Sao Paulo, Dept Comp & Digital Syst Engn, Escola Politecn, Av Prof Luciano Gualberto,Travessa 3,158 Butanta, BR-05508010 Sao Paulo, SP, Brazil
dc.description.affiliationUniv Paulista, Dept ICET, Ave Presidente Juscelino Kubitschek Oliveira S-N, BR-15091450 Sao Jose Do Rio Preto, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista UNESP, Dept Comp Sci & Stat, Rua Cristovelo Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipUniversidade Paulista (Unip/ICET)
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipIdFAPESP: 2019/00030-3
dc.description.sponsorshipIdUniversidade Paulista (Unip/ICET): 7-03-1169/2021
dc.format.extent167-174
dc.identifierhttp://dx.doi.org/10.5220/0011082900003179
dc.identifier.citationIceis: Proceedings Of The 24th International Conference On Enterprise Information Systems - Vol 2. Setubal: Scitepress, p. 167-174, 2022.
dc.identifier.doi10.5220/0011082900003179
dc.identifier.urihttp://hdl.handle.net/11449/237766
dc.identifier.wosWOS:000814767900018
dc.language.isoeng
dc.publisherScitepress
dc.relation.ispartofIceis: Proceedings Of The 24th International Conference On Enterprise Information Systems - Vol 2
dc.sourceWeb of Science
dc.subjectBioinformatics
dc.subjectMultiple Sequence Alignment
dc.subjectGenetic Algorithm
dc.subjectHybrid Multiple Sequence Alignment
dc.titleA Hybrid Genetic Algorithm using Progressive Alignment and Consistency based Approach for Multiple Sequence Alignmentsen
dc.typeTrabalho apresentado em evento
dcterms.rightsHolderScitepress

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Artigos - Ciência da Computação e Estatística - IBILCE