Unsupervised modeling of cell morphology dynamics for time-lapse microscopy

Autores
Zhong, Qing; Busetto, Alberto Giovanni; Fededa, Juan Pablo; Buhmann, Joachim M.; Gerlich, Daniel W.
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Analysis of cellular phenotypes in large imaging data sets conventionally involves supervised statistical methods, which require user-annotated training data. This paper introduces an unsupervised learning method, based on temporally constrained combinatorial clustering, for automatic prediction of cell morphology classes in time-resolved images. We applied the unsupervised method to diverse fluorescent markers and screening data and validated accurate classification of human cell phenotypes, demonstrating fully objective data labeling in image-based systems biology.
Fil: Zhong, Qing. Swiss Federal Institute Of Technology Zurich; Suiza
Fil: Busetto, Alberto Giovanni. Swiss Federal Institute Of Technology Zurich; Suiza
Fil: Fededa, Juan Pablo. Swiss Federal Institute Of Technology Zurich; Suiza. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Buhmann, Joachim M.. Swiss Federal Institute Of Technology Zurich; Suiza
Fil: Gerlich, Daniel W.. Swiss Federal Institute Of Technology Zurich; Suiza
Materia
Unsupervised Modeling
Time-Lapse Microscopy
Cell Morphology Dynamics
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/21074
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/21074
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Unsupervised modeling of cell morphology dynamics for time-lapse microscopyZhong, QingBusetto, Alberto GiovanniFededa, Juan PabloBuhmann, Joachim M.Gerlich, Daniel W.Unsupervised ModelingTime-Lapse MicroscopyCell Morphology Dynamicshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Analysis of cellular phenotypes in large imaging data sets conventionally involves supervised statistical methods, which require user-annotated training data. This paper introduces an unsupervised learning method, based on temporally constrained combinatorial clustering, for automatic prediction of cell morphology classes in time-resolved images. We applied the unsupervised method to diverse fluorescent markers and screening data and validated accurate classification of human cell phenotypes, demonstrating fully objective data labeling in image-based systems biology.Fil: Zhong, Qing. Swiss Federal Institute Of Technology Zurich; SuizaFil: Busetto, Alberto Giovanni. Swiss Federal Institute Of Technology Zurich; SuizaFil: Fededa, Juan Pablo. Swiss Federal Institute Of Technology Zurich; Suiza. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Buhmann, Joachim M.. Swiss Federal Institute Of Technology Zurich; SuizaFil: Gerlich, Daniel W.. Swiss Federal Institute Of Technology Zurich; SuizaNature Publishing Group2012-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/21074Zhong, Qing; Busetto, Alberto Giovanni; Fededa, Juan Pablo; Buhmann, Joachim M.; Gerlich, Daniel W.; Unsupervised modeling of cell morphology dynamics for time-lapse microscopy; Nature Publishing Group; Nature Methods; 9; 7; 5-2012; 711-7131548-70911548-7105CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1038/nmeth.2046info:eu-repo/semantics/altIdentifier/url/http://www.nature.com/nmeth/journal/v9/n7/full/nmeth.2046.htmlinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:24:15Zoai:ri.conicet.gov.ar:11336/21074instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:24:15.337CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Unsupervised modeling of cell morphology dynamics for time-lapse microscopy
title Unsupervised modeling of cell morphology dynamics for time-lapse microscopy
spellingShingle Unsupervised modeling of cell morphology dynamics for time-lapse microscopy
Zhong, Qing
Unsupervised Modeling
Time-Lapse Microscopy
Cell Morphology Dynamics
title_short Unsupervised modeling of cell morphology dynamics for time-lapse microscopy
title_full Unsupervised modeling of cell morphology dynamics for time-lapse microscopy
title_fullStr Unsupervised modeling of cell morphology dynamics for time-lapse microscopy
title_full_unstemmed Unsupervised modeling of cell morphology dynamics for time-lapse microscopy
title_sort Unsupervised modeling of cell morphology dynamics for time-lapse microscopy
dc.creator.none.fl_str_mv Zhong, Qing
Busetto, Alberto Giovanni
Fededa, Juan Pablo
Buhmann, Joachim M.
Gerlich, Daniel W.
author Zhong, Qing
author_facet Zhong, Qing
Busetto, Alberto Giovanni
Fededa, Juan Pablo
Buhmann, Joachim M.
Gerlich, Daniel W.
author_role author
author2 Busetto, Alberto Giovanni
Fededa, Juan Pablo
Buhmann, Joachim M.
Gerlich, Daniel W.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Unsupervised Modeling
Time-Lapse Microscopy
Cell Morphology Dynamics
topic Unsupervised Modeling
Time-Lapse Microscopy
Cell Morphology Dynamics
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Analysis of cellular phenotypes in large imaging data sets conventionally involves supervised statistical methods, which require user-annotated training data. This paper introduces an unsupervised learning method, based on temporally constrained combinatorial clustering, for automatic prediction of cell morphology classes in time-resolved images. We applied the unsupervised method to diverse fluorescent markers and screening data and validated accurate classification of human cell phenotypes, demonstrating fully objective data labeling in image-based systems biology.
Fil: Zhong, Qing. Swiss Federal Institute Of Technology Zurich; Suiza
Fil: Busetto, Alberto Giovanni. Swiss Federal Institute Of Technology Zurich; Suiza
Fil: Fededa, Juan Pablo. Swiss Federal Institute Of Technology Zurich; Suiza. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Buhmann, Joachim M.. Swiss Federal Institute Of Technology Zurich; Suiza
Fil: Gerlich, Daniel W.. Swiss Federal Institute Of Technology Zurich; Suiza
description Analysis of cellular phenotypes in large imaging data sets conventionally involves supervised statistical methods, which require user-annotated training data. This paper introduces an unsupervised learning method, based on temporally constrained combinatorial clustering, for automatic prediction of cell morphology classes in time-resolved images. We applied the unsupervised method to diverse fluorescent markers and screening data and validated accurate classification of human cell phenotypes, demonstrating fully objective data labeling in image-based systems biology.
publishDate 2012
dc.date.none.fl_str_mv 2012-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/21074
Zhong, Qing; Busetto, Alberto Giovanni; Fededa, Juan Pablo; Buhmann, Joachim M.; Gerlich, Daniel W.; Unsupervised modeling of cell morphology dynamics for time-lapse microscopy; Nature Publishing Group; Nature Methods; 9; 7; 5-2012; 711-713
1548-7091
1548-7105
CONICET Digital
CONICET
url http://hdl.handle.net/11336/21074
identifier_str_mv Zhong, Qing; Busetto, Alberto Giovanni; Fededa, Juan Pablo; Buhmann, Joachim M.; Gerlich, Daniel W.; Unsupervised modeling of cell morphology dynamics for time-lapse microscopy; Nature Publishing Group; Nature Methods; 9; 7; 5-2012; 711-713
1548-7091
1548-7105
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1038/nmeth.2046
info:eu-repo/semantics/altIdentifier/url/http://www.nature.com/nmeth/journal/v9/n7/full/nmeth.2046.html
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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