Genetic algorithm for analysis of mutations in Parkinson’s disease
Autor(a) principal: | |
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Data de Publicação: | 2005 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UCB |
Texto Completo: | http://twingo.ucb.br:8080/jspui/handle/10869/423 https://repositorio.ucb.br:9443/jspui/handle/123456789/7616 |
Resumo: | Mitochondrial genetics has unique features that impede analysis of the biological significance of mitochondrial mutations. Simple searches for differences in total mutational load between normal and pathological samples have been frequently unrewarding, raising the possibility that more complex patterns of mutations may be responsible for some conditions. We explore this possibility in the context of Parkinson’s disease (PD). Methods and materials: We report the development of a modified genetic algorithm suited for detection of biologically meaningful patterns of mitochondrial mutations. The algorithm is applied to a database of mutations derived from biological samples, and verified by the use of shuffled data, and repeated leave-one-out testing. Results: It is possible to derive, from a very small sample, multiple accurate classifier functions that correlate with biological features. The methodology is validated statistically through experiments with fabricated data. Conclusion: This algorithm might be generally applicable to conditions where interactions among multiple mitochondrial DNA mutations are important. The patterns embodied in the classifier functions obtained should be the subject of further experimental study. |
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Smigrodzki, RafalGoertzel, BenPennachin, CassioCoelho, LúcioProsdocimi, FranciscoParker Jr., W. Davis2016-10-10T03:52:06Z2016-10-10T03:52:06Z2005SMIGRODZKI, Rafal et al. Genetic algorithm for analysis of mutations in Parkinson’s disease. Artificial Intelligence in Medicine, v. 35, n.3, p. 227-241, 2005.9333657http://twingo.ucb.br:8080/jspui/handle/10869/423https://repositorio.ucb.br:9443/jspui/handle/123456789/7616Mitochondrial genetics has unique features that impede analysis of the biological significance of mitochondrial mutations. Simple searches for differences in total mutational load between normal and pathological samples have been frequently unrewarding, raising the possibility that more complex patterns of mutations may be responsible for some conditions. We explore this possibility in the context of Parkinson’s disease (PD). Methods and materials: We report the development of a modified genetic algorithm suited for detection of biologically meaningful patterns of mitochondrial mutations. The algorithm is applied to a database of mutations derived from biological samples, and verified by the use of shuffled data, and repeated leave-one-out testing. Results: It is possible to derive, from a very small sample, multiple accurate classifier functions that correlate with biological features. The methodology is validated statistically through experiments with fabricated data. Conclusion: This algorithm might be generally applicable to conditions where interactions among multiple mitochondrial DNA mutations are important. The patterns embodied in the classifier functions obtained should be the subject of further experimental study.Made available in DSpace on 2016-10-10T03:52:06Z (GMT). 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dc.title.pt_BR.fl_str_mv |
Genetic algorithm for analysis of mutations in Parkinson’s disease |
title |
Genetic algorithm for analysis of mutations in Parkinson’s disease |
spellingShingle |
Genetic algorithm for analysis of mutations in Parkinson’s disease Smigrodzki, Rafal Genetic algorithm Mitochondrial DNA Pattern recognition Parkinson’s disease |
title_short |
Genetic algorithm for analysis of mutations in Parkinson’s disease |
title_full |
Genetic algorithm for analysis of mutations in Parkinson’s disease |
title_fullStr |
Genetic algorithm for analysis of mutations in Parkinson’s disease |
title_full_unstemmed |
Genetic algorithm for analysis of mutations in Parkinson’s disease |
title_sort |
Genetic algorithm for analysis of mutations in Parkinson’s disease |
author |
Smigrodzki, Rafal |
author_facet |
Smigrodzki, Rafal Goertzel, Ben Pennachin, Cassio Coelho, Lúcio Prosdocimi, Francisco Parker Jr., W. Davis |
author_role |
author |
author2 |
Goertzel, Ben Pennachin, Cassio Coelho, Lúcio Prosdocimi, Francisco Parker Jr., W. Davis |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Smigrodzki, Rafal Goertzel, Ben Pennachin, Cassio Coelho, Lúcio Prosdocimi, Francisco Parker Jr., W. Davis |
dc.subject.por.fl_str_mv |
Genetic algorithm Mitochondrial DNA Pattern recognition Parkinson’s disease |
topic |
Genetic algorithm Mitochondrial DNA Pattern recognition Parkinson’s disease |
dc.description.abstract.por.fl_txt_mv |
Mitochondrial genetics has unique features that impede analysis of the biological significance of mitochondrial mutations. Simple searches for differences in total mutational load between normal and pathological samples have been frequently unrewarding, raising the possibility that more complex patterns of mutations may be responsible for some conditions. We explore this possibility in the context of Parkinson’s disease (PD). Methods and materials: We report the development of a modified genetic algorithm suited for detection of biologically meaningful patterns of mitochondrial mutations. The algorithm is applied to a database of mutations derived from biological samples, and verified by the use of shuffled data, and repeated leave-one-out testing. Results: It is possible to derive, from a very small sample, multiple accurate classifier functions that correlate with biological features. The methodology is validated statistically through experiments with fabricated data. Conclusion: This algorithm might be generally applicable to conditions where interactions among multiple mitochondrial DNA mutations are important. The patterns embodied in the classifier functions obtained should be the subject of further experimental study. |
dc.description.version.pt_BR.fl_txt_mv |
Sim |
dc.description.status.pt_BR.fl_txt_mv |
Publicado |
description |
Mitochondrial genetics has unique features that impede analysis of the biological significance of mitochondrial mutations. Simple searches for differences in total mutational load between normal and pathological samples have been frequently unrewarding, raising the possibility that more complex patterns of mutations may be responsible for some conditions. We explore this possibility in the context of Parkinson’s disease (PD). Methods and materials: We report the development of a modified genetic algorithm suited for detection of biologically meaningful patterns of mitochondrial mutations. The algorithm is applied to a database of mutations derived from biological samples, and verified by the use of shuffled data, and repeated leave-one-out testing. Results: It is possible to derive, from a very small sample, multiple accurate classifier functions that correlate with biological features. The methodology is validated statistically through experiments with fabricated data. Conclusion: This algorithm might be generally applicable to conditions where interactions among multiple mitochondrial DNA mutations are important. The patterns embodied in the classifier functions obtained should be the subject of further experimental study. |
publishDate |
2005 |
dc.date.issued.fl_str_mv |
2005 |
dc.date.accessioned.fl_str_mv |
2016-10-10T03:52:06Z |
dc.date.available.fl_str_mv |
2016-10-10T03:52:06Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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publishedVersion |
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article |
dc.identifier.citation.fl_str_mv |
SMIGRODZKI, Rafal et al. Genetic algorithm for analysis of mutations in Parkinson’s disease. Artificial Intelligence in Medicine, v. 35, n.3, p. 227-241, 2005. |
dc.identifier.uri.fl_str_mv |
http://twingo.ucb.br:8080/jspui/handle/10869/423 https://repositorio.ucb.br:9443/jspui/handle/123456789/7616 |
dc.identifier.issn.none.fl_str_mv |
9333657 |
identifier_str_mv |
SMIGRODZKI, Rafal et al. Genetic algorithm for analysis of mutations in Parkinson’s disease. Artificial Intelligence in Medicine, v. 35, n.3, p. 227-241, 2005. 9333657 |
url |
http://twingo.ucb.br:8080/jspui/handle/10869/423 https://repositorio.ucb.br:9443/jspui/handle/123456789/7616 |
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eng |
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eng |
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Restrito UCB info:eu-repo/semantics/openAccess |
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Universidade Católica de Brasília (UCB) |
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