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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/21074
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:55:08Zoai: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:34982025-09-29 09:55:09.267CONICET 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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1844613664976404480 |
score |
13.070432 |