A biologically-inspired validity measure for comparison of clustering methods over metabolic datasets

Autores
Stegmayer, Georgina; Milone, Diego Humberto; Kamenetzky, Laura; Lopez, Mariana Gabriela; Carrari, Fernando Oscar
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In the biological domain, clustering is based on the assumption that genes or metabolites involved in a common biological process are coexpressed/coaccumulated under the control of the same regulatory network. Thus, a detailed inspection of the grouped patterns to verify their memberships to well-known metabolic pathways could be very useful for the evaluation of clusters from a biological perspective. The aim of this work is to propose a novel approach for the comparison of clustering methods over metabolic data sets, including prior biological knowledge about the relation among elements that constitute the clusters. A way of measuring the biological significance of clustering solutions is proposed. This is addressed from the perspective of the usefulness of the clusters to identify those patterns that change in coordination and belong to common pathways of metabolic regulation. The measure summarizes in a compact way the objective analysis of clustering methods, which respects coherence and clusters distribution. It also evaluates the biological internal connections of such clusters considering common pathways. The proposed measure was tested in two biological databases using three clustering methods.
Fil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Kamenetzky, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Lopez, Mariana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Carrari, Fernando Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Clustering
validation measure
biological assessment
metabolic pathways
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:11336/236594
Acceder
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network_name_str CONICET Digital (CONICET)
spelling A biologically-inspired validity measure for comparison of clustering methods over metabolic datasetsStegmayer, GeorginaMilone, Diego HumbertoKamenetzky, LauraLopez, Mariana GabrielaCarrari, Fernando OscarClusteringvalidation measurebiological assessmentmetabolic pathwayshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In the biological domain, clustering is based on the assumption that genes or metabolites involved in a common biological process are coexpressed/coaccumulated under the control of the same regulatory network. Thus, a detailed inspection of the grouped patterns to verify their memberships to well-known metabolic pathways could be very useful for the evaluation of clusters from a biological perspective. The aim of this work is to propose a novel approach for the comparison of clustering methods over metabolic data sets, including prior biological knowledge about the relation among elements that constitute the clusters. A way of measuring the biological significance of clustering solutions is proposed. This is addressed from the perspective of the usefulness of the clusters to identify those patterns that change in coordination and belong to common pathways of metabolic regulation. The measure summarizes in a compact way the objective analysis of clustering methods, which respects coherence and clusters distribution. It also evaluates the biological internal connections of such clusters considering common pathways. The proposed measure was tested in two biological databases using three clustering methods.Fil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Kamenetzky, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lopez, Mariana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Carrari, Fernando Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaIEEE Computer Society2012-01info: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/11336/236594Stegmayer, Georgina; Milone, Diego Humberto; Kamenetzky, Laura; Lopez, Mariana Gabriela; Carrari, Fernando Oscar; A biologically-inspired validity measure for comparison of clustering methods over metabolic datasets; IEEE Computer Society; Ieee-acm Transactions On Computational Biology And Bioinformatics; 9; 3; 1-2012; 706-7161545-5963CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/6127857info:eu-repo/semantics/altIdentifier/doi/10.1109/TCBB.2012.10info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:18:53Zoai:ri.conicet.gov.ar:11336/236594instacron: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 10:18:53.727CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A biologically-inspired validity measure for comparison of clustering methods over metabolic datasets
title A biologically-inspired validity measure for comparison of clustering methods over metabolic datasets
spellingShingle A biologically-inspired validity measure for comparison of clustering methods over metabolic datasets
Stegmayer, Georgina
Clustering
validation measure
biological assessment
metabolic pathways
title_short A biologically-inspired validity measure for comparison of clustering methods over metabolic datasets
title_full A biologically-inspired validity measure for comparison of clustering methods over metabolic datasets
title_fullStr A biologically-inspired validity measure for comparison of clustering methods over metabolic datasets
title_full_unstemmed A biologically-inspired validity measure for comparison of clustering methods over metabolic datasets
title_sort A biologically-inspired validity measure for comparison of clustering methods over metabolic datasets
dc.creator.none.fl_str_mv Stegmayer, Georgina
Milone, Diego Humberto
Kamenetzky, Laura
Lopez, Mariana Gabriela
Carrari, Fernando Oscar
author Stegmayer, Georgina
author_facet Stegmayer, Georgina
Milone, Diego Humberto
Kamenetzky, Laura
Lopez, Mariana Gabriela
Carrari, Fernando Oscar
author_role author
author2 Milone, Diego Humberto
Kamenetzky, Laura
Lopez, Mariana Gabriela
Carrari, Fernando Oscar
author2_role author
author
author
author
dc.subject.none.fl_str_mv Clustering
validation measure
biological assessment
metabolic pathways
topic Clustering
validation measure
biological assessment
metabolic pathways
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In the biological domain, clustering is based on the assumption that genes or metabolites involved in a common biological process are coexpressed/coaccumulated under the control of the same regulatory network. Thus, a detailed inspection of the grouped patterns to verify their memberships to well-known metabolic pathways could be very useful for the evaluation of clusters from a biological perspective. The aim of this work is to propose a novel approach for the comparison of clustering methods over metabolic data sets, including prior biological knowledge about the relation among elements that constitute the clusters. A way of measuring the biological significance of clustering solutions is proposed. This is addressed from the perspective of the usefulness of the clusters to identify those patterns that change in coordination and belong to common pathways of metabolic regulation. The measure summarizes in a compact way the objective analysis of clustering methods, which respects coherence and clusters distribution. It also evaluates the biological internal connections of such clusters considering common pathways. The proposed measure was tested in two biological databases using three clustering methods.
Fil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Kamenetzky, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Lopez, Mariana Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Carrari, Fernando Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description In the biological domain, clustering is based on the assumption that genes or metabolites involved in a common biological process are coexpressed/coaccumulated under the control of the same regulatory network. Thus, a detailed inspection of the grouped patterns to verify their memberships to well-known metabolic pathways could be very useful for the evaluation of clusters from a biological perspective. The aim of this work is to propose a novel approach for the comparison of clustering methods over metabolic data sets, including prior biological knowledge about the relation among elements that constitute the clusters. A way of measuring the biological significance of clustering solutions is proposed. This is addressed from the perspective of the usefulness of the clusters to identify those patterns that change in coordination and belong to common pathways of metabolic regulation. The measure summarizes in a compact way the objective analysis of clustering methods, which respects coherence and clusters distribution. It also evaluates the biological internal connections of such clusters considering common pathways. The proposed measure was tested in two biological databases using three clustering methods.
publishDate 2012
dc.date.none.fl_str_mv 2012-01
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/11336/236594
Stegmayer, Georgina; Milone, Diego Humberto; Kamenetzky, Laura; Lopez, Mariana Gabriela; Carrari, Fernando Oscar; A biologically-inspired validity measure for comparison of clustering methods over metabolic datasets; IEEE Computer Society; Ieee-acm Transactions On Computational Biology And Bioinformatics; 9; 3; 1-2012; 706-716
1545-5963
CONICET Digital
CONICET
url http://hdl.handle.net/11336/236594
identifier_str_mv Stegmayer, Georgina; Milone, Diego Humberto; Kamenetzky, Laura; Lopez, Mariana Gabriela; Carrari, Fernando Oscar; A biologically-inspired validity measure for comparison of clustering methods over metabolic datasets; IEEE Computer Society; Ieee-acm Transactions On Computational Biology And Bioinformatics; 9; 3; 1-2012; 706-716
1545-5963
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/6127857
info:eu-repo/semantics/altIdentifier/doi/10.1109/TCBB.2012.10
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 IEEE Computer Society
publisher.none.fl_str_mv IEEE Computer Society
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 13.070432

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