Optimization of artificial neural network by genetic algorithm for describing viral production from uniform design data

Detalhes bibliográficos
Autor(a) principal: Takahashi, Maria Beatriz [UNESP]
Data de Publicação: 2016
Outros Autores: Rocha, Jose Celso [UNESP], Fernandez Nunez, Eutimio Gustavo [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.procbio.2015.12.005
http://hdl.handle.net/11449/161270
Resumo: This work objective was to define a modeling approach based on genetic algorithm (GA) for optimizing parameters of an artificial neural network (ANN); the latter describes rabies virus production in BHK-21 cells based on empirical data derived from uniform designs (UDs) with different numbers of experimental runs. The parameters considered for viral infection were temperature (34 and 37 degrees C), multiplicity of infection (0.04, 0.07, and 0.1), infection, and harvest times (24, 48, and 72 h), with virus production as the monitored output variable. A multilevel factorial experimental design was performed and used to train, validate, and test the ANN. Its experimental fractions (18, 24, 30, 36, and 42 runs) defined by UDs were used to simulate the neural architectures. In GA, the neural computing parameters constituted the population individuals, and the steps involved were population creation, selection, and replacement by crossover and mutation. The ANN optimized by the combined algorithm showed a good calibration for all UDs under consideration, thus demonstrating to be suitable (R>0.85) as a correlation method in UDs independent of the experimental runs developed. Therefore, this work could guide researchers in the efficient use of UDs in the simulation and optimization of virus production processes. (C) 2015 Elsevier Ltd. All rights reserved.
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spelling Optimization of artificial neural network by genetic algorithm for describing viral production from uniform design dataArtificial neural networkBioprocessGenetic algorithmUniform designVirus productionThis work objective was to define a modeling approach based on genetic algorithm (GA) for optimizing parameters of an artificial neural network (ANN); the latter describes rabies virus production in BHK-21 cells based on empirical data derived from uniform designs (UDs) with different numbers of experimental runs. The parameters considered for viral infection were temperature (34 and 37 degrees C), multiplicity of infection (0.04, 0.07, and 0.1), infection, and harvest times (24, 48, and 72 h), with virus production as the monitored output variable. A multilevel factorial experimental design was performed and used to train, validate, and test the ANN. Its experimental fractions (18, 24, 30, 36, and 42 runs) defined by UDs were used to simulate the neural architectures. In GA, the neural computing parameters constituted the population individuals, and the steps involved were population creation, selection, and replacement by crossover and mutation. The ANN optimized by the combined algorithm showed a good calibration for all UDs under consideration, thus demonstrating to be suitable (R>0.85) as a correlation method in UDs independent of the experimental runs developed. Therefore, this work could guide researchers in the efficient use of UDs in the simulation and optimization of virus production processes. (C) 2015 Elsevier Ltd. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Univ Estadual Julio de Mesquita Filho Campus Assi, Dept Ciencias Biol, Ave Dom Antonio 2100, BR-19806900 Assis, SP, BrazilUniv Estadual Julio de Mesquita Filho Campus Assi, Dept Ciencias Biol, Ave Dom Antonio 2100, BR-19806900 Assis, SP, BrazilFAPESP: 2010/52521-6Elsevier B.V.Universidade Estadual Paulista (Unesp)Takahashi, Maria Beatriz [UNESP]Rocha, Jose Celso [UNESP]Fernandez Nunez, Eutimio Gustavo [UNESP]2018-11-26T16:27:50Z2018-11-26T16:27:50Z2016-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article422-430application/pdfhttp://dx.doi.org/10.1016/j.procbio.2015.12.005Process Biochemistry. Oxford: Elsevier Sci Ltd, v. 51, n. 3, p. 422-430, 2016.1359-5113http://hdl.handle.net/11449/16127010.1016/j.procbio.2015.12.005WOS:000371558600011WOS000371558600011.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProcess Biochemistry0,761info:eu-repo/semantics/openAccess2023-11-11T06:11:49Zoai:repositorio.unesp.br:11449/161270Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-11-11T06:11:49Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Optimization of artificial neural network by genetic algorithm for describing viral production from uniform design data
title Optimization of artificial neural network by genetic algorithm for describing viral production from uniform design data
spellingShingle Optimization of artificial neural network by genetic algorithm for describing viral production from uniform design data
Takahashi, Maria Beatriz [UNESP]
Artificial neural network
Bioprocess
Genetic algorithm
Uniform design
Virus production
title_short Optimization of artificial neural network by genetic algorithm for describing viral production from uniform design data
title_full Optimization of artificial neural network by genetic algorithm for describing viral production from uniform design data
title_fullStr Optimization of artificial neural network by genetic algorithm for describing viral production from uniform design data
title_full_unstemmed Optimization of artificial neural network by genetic algorithm for describing viral production from uniform design data
title_sort Optimization of artificial neural network by genetic algorithm for describing viral production from uniform design data
author Takahashi, Maria Beatriz [UNESP]
author_facet Takahashi, Maria Beatriz [UNESP]
Rocha, Jose Celso [UNESP]
Fernandez Nunez, Eutimio Gustavo [UNESP]
author_role author
author2 Rocha, Jose Celso [UNESP]
Fernandez Nunez, Eutimio Gustavo [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Takahashi, Maria Beatriz [UNESP]
Rocha, Jose Celso [UNESP]
Fernandez Nunez, Eutimio Gustavo [UNESP]
dc.subject.por.fl_str_mv Artificial neural network
Bioprocess
Genetic algorithm
Uniform design
Virus production
topic Artificial neural network
Bioprocess
Genetic algorithm
Uniform design
Virus production
description This work objective was to define a modeling approach based on genetic algorithm (GA) for optimizing parameters of an artificial neural network (ANN); the latter describes rabies virus production in BHK-21 cells based on empirical data derived from uniform designs (UDs) with different numbers of experimental runs. The parameters considered for viral infection were temperature (34 and 37 degrees C), multiplicity of infection (0.04, 0.07, and 0.1), infection, and harvest times (24, 48, and 72 h), with virus production as the monitored output variable. A multilevel factorial experimental design was performed and used to train, validate, and test the ANN. Its experimental fractions (18, 24, 30, 36, and 42 runs) defined by UDs were used to simulate the neural architectures. In GA, the neural computing parameters constituted the population individuals, and the steps involved were population creation, selection, and replacement by crossover and mutation. The ANN optimized by the combined algorithm showed a good calibration for all UDs under consideration, thus demonstrating to be suitable (R>0.85) as a correlation method in UDs independent of the experimental runs developed. Therefore, this work could guide researchers in the efficient use of UDs in the simulation and optimization of virus production processes. (C) 2015 Elsevier Ltd. All rights reserved.
publishDate 2016
dc.date.none.fl_str_mv 2016-03-01
2018-11-26T16:27:50Z
2018-11-26T16:27:50Z
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://dx.doi.org/10.1016/j.procbio.2015.12.005
Process Biochemistry. Oxford: Elsevier Sci Ltd, v. 51, n. 3, p. 422-430, 2016.
1359-5113
http://hdl.handle.net/11449/161270
10.1016/j.procbio.2015.12.005
WOS:000371558600011
WOS000371558600011.pdf
url http://dx.doi.org/10.1016/j.procbio.2015.12.005
http://hdl.handle.net/11449/161270
identifier_str_mv Process Biochemistry. Oxford: Elsevier Sci Ltd, v. 51, n. 3, p. 422-430, 2016.
1359-5113
10.1016/j.procbio.2015.12.005
WOS:000371558600011
WOS000371558600011.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Process Biochemistry
0,761
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 422-430
application/pdf
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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