Spot defects detection in cDNA microarray images

Autores
Larese, Monica Graciela; Granitto, Pablo Miguel; Gomez, Juan Carlos
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Bad quality spots should be filtered out at early steps in microarray analysis to avoid noisy data. In this paper we implement quality control of individual spots from real microarray images. First of all, we consider the binary classification problem of detecting bad quality spots. We propose the use of ensemble algorithms to perform detection and obtain improved accuracies over previous studies in the literature. Next, we analyze the untackled problem of identifying specific spot defects. One spot may have several faults simultaneously (or none of them) yielding a multi-label classification problem. We propose several extra features in addition to those used for binary classification, and we use three different methods to perform the classification task: five independent binary classifiers, the recent Convex Multi-task Feature Learning (CMFL) algorithm and Convex Multi-task Independent Learning (CMIL). We analyze the Hamming loss and areas under the receiver operating characteristic (ROC) curves to quantify the accuracies of the methods. We find that the three strategies achieve similar results leading to a successful identification of particular defects. Also, using a Random forest-based analysis we show that the newly introduced features are highly relevant for this task.
Fil: Larese, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Gomez, Juan Carlos. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura. Escuela de Ingeniería Electrónica. Departamento de Control. Laboratorio de Sistemas Dinámicos y Procesamiento de Información; Argentina
Materia
MICROARRAY IMAGES
QUALITY CONTROL
DEFECTS IDENTIFICATION
ENSEMBLE CLASSIFIERS
CONVEX MULTI-TASK LEARNING
PATTERN RECOGNITION
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:11336/113381
Acceder
id CONICETDig_2e05ee4ddffe4400c56a5e3f8342999c
oai_identifier_str oai:ri.conicet.gov.ar:11336/113381
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Spot defects detection in cDNA microarray imagesLarese, Monica GracielaGranitto, Pablo MiguelGomez, Juan CarlosMICROARRAY IMAGESQUALITY CONTROLDEFECTS IDENTIFICATIONENSEMBLE CLASSIFIERSCONVEX MULTI-TASK LEARNINGPATTERN RECOGNITIONhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Bad quality spots should be filtered out at early steps in microarray analysis to avoid noisy data. In this paper we implement quality control of individual spots from real microarray images. First of all, we consider the binary classification problem of detecting bad quality spots. We propose the use of ensemble algorithms to perform detection and obtain improved accuracies over previous studies in the literature. Next, we analyze the untackled problem of identifying specific spot defects. One spot may have several faults simultaneously (or none of them) yielding a multi-label classification problem. We propose several extra features in addition to those used for binary classification, and we use three different methods to perform the classification task: five independent binary classifiers, the recent Convex Multi-task Feature Learning (CMFL) algorithm and Convex Multi-task Independent Learning (CMIL). We analyze the Hamming loss and areas under the receiver operating characteristic (ROC) curves to quantify the accuracies of the methods. We find that the three strategies achieve similar results leading to a successful identification of particular defects. Also, using a Random forest-based analysis we show that the newly introduced features are highly relevant for this task.Fil: Larese, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Gomez, Juan Carlos. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura. Escuela de Ingeniería Electrónica. Departamento de Control. Laboratorio de Sistemas Dinámicos y Procesamiento de Información; ArgentinaSpringer2011-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/113381Larese, Monica Graciela; Granitto, Pablo Miguel; Gomez, Juan Carlos; Spot defects detection in cDNA microarray images; Springer; Pattern Analysis And Applications; 16; 3; 8-2011; 307-3191433-7541CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/s10044-011-0234-xinfo:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s10044-011-0234-xinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:56:33Zoai:ri.conicet.gov.ar:11336/113381instacron: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 09:56:34.142CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Spot defects detection in cDNA microarray images
title Spot defects detection in cDNA microarray images
spellingShingle Spot defects detection in cDNA microarray images
Larese, Monica Graciela
MICROARRAY IMAGES
QUALITY CONTROL
DEFECTS IDENTIFICATION
ENSEMBLE CLASSIFIERS
CONVEX MULTI-TASK LEARNING
PATTERN RECOGNITION
title_short Spot defects detection in cDNA microarray images
title_full Spot defects detection in cDNA microarray images
title_fullStr Spot defects detection in cDNA microarray images
title_full_unstemmed Spot defects detection in cDNA microarray images
title_sort Spot defects detection in cDNA microarray images
dc.creator.none.fl_str_mv Larese, Monica Graciela
Granitto, Pablo Miguel
Gomez, Juan Carlos
author Larese, Monica Graciela
author_facet Larese, Monica Graciela
Granitto, Pablo Miguel
Gomez, Juan Carlos
author_role author
author2 Granitto, Pablo Miguel
Gomez, Juan Carlos
author2_role author
author
dc.subject.none.fl_str_mv MICROARRAY IMAGES
QUALITY CONTROL
DEFECTS IDENTIFICATION
ENSEMBLE CLASSIFIERS
CONVEX MULTI-TASK LEARNING
PATTERN RECOGNITION
topic MICROARRAY IMAGES
QUALITY CONTROL
DEFECTS IDENTIFICATION
ENSEMBLE CLASSIFIERS
CONVEX MULTI-TASK LEARNING
PATTERN RECOGNITION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Bad quality spots should be filtered out at early steps in microarray analysis to avoid noisy data. In this paper we implement quality control of individual spots from real microarray images. First of all, we consider the binary classification problem of detecting bad quality spots. We propose the use of ensemble algorithms to perform detection and obtain improved accuracies over previous studies in the literature. Next, we analyze the untackled problem of identifying specific spot defects. One spot may have several faults simultaneously (or none of them) yielding a multi-label classification problem. We propose several extra features in addition to those used for binary classification, and we use three different methods to perform the classification task: five independent binary classifiers, the recent Convex Multi-task Feature Learning (CMFL) algorithm and Convex Multi-task Independent Learning (CMIL). We analyze the Hamming loss and areas under the receiver operating characteristic (ROC) curves to quantify the accuracies of the methods. We find that the three strategies achieve similar results leading to a successful identification of particular defects. Also, using a Random forest-based analysis we show that the newly introduced features are highly relevant for this task.
Fil: Larese, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Granitto, Pablo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
Fil: Gomez, Juan Carlos. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura. Escuela de Ingeniería Electrónica. Departamento de Control. Laboratorio de Sistemas Dinámicos y Procesamiento de Información; Argentina
description Bad quality spots should be filtered out at early steps in microarray analysis to avoid noisy data. In this paper we implement quality control of individual spots from real microarray images. First of all, we consider the binary classification problem of detecting bad quality spots. We propose the use of ensemble algorithms to perform detection and obtain improved accuracies over previous studies in the literature. Next, we analyze the untackled problem of identifying specific spot defects. One spot may have several faults simultaneously (or none of them) yielding a multi-label classification problem. We propose several extra features in addition to those used for binary classification, and we use three different methods to perform the classification task: five independent binary classifiers, the recent Convex Multi-task Feature Learning (CMFL) algorithm and Convex Multi-task Independent Learning (CMIL). We analyze the Hamming loss and areas under the receiver operating characteristic (ROC) curves to quantify the accuracies of the methods. We find that the three strategies achieve similar results leading to a successful identification of particular defects. Also, using a Random forest-based analysis we show that the newly introduced features are highly relevant for this task.
publishDate 2011
dc.date.none.fl_str_mv 2011-08
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/11336/113381
Larese, Monica Graciela; Granitto, Pablo Miguel; Gomez, Juan Carlos; Spot defects detection in cDNA microarray images; Springer; Pattern Analysis And Applications; 16; 3; 8-2011; 307-319
1433-7541
CONICET Digital
CONICET
url http://hdl.handle.net/11336/113381
identifier_str_mv Larese, Monica Graciela; Granitto, Pablo Miguel; Gomez, Juan Carlos; Spot defects detection in cDNA microarray images; Springer; Pattern Analysis And Applications; 16; 3; 8-2011; 307-319
1433-7541
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/s10044-011-0234-x
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s10044-011-0234-x
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
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
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
_version_ 1844613698005499904
score 13.070432

Publicaciones similares