Optimization of artificial neural network by genetic algorithm for describing viral production from uniform design data
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
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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Repositório Institucional da UNESP |
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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 |
|
_version_ |
1799964895198838784 |