On Artificial Gene Regulatory Networks

Autores
Carballido, Jessica Andrea; Ponzoni, Ignacio
Año de publicación
2008
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Gene regulatory networks (GRNs) represent dependencies between genes and their products during protein synthesis at the molecular level. At the present there exist many inference methods that infer GRNs form observed data. However, gene expression data sets have in general considerable noise that make understanding and learning even simple regulatory patterns difficult. Also, there is no well-known method to test the accuracy of inferred GRNs. Given these drawbacks, characterizing the effectiveness of different techniques to uncover gene networks remains a challenge. The development of artificial GRNs with known biological features of expression complexity, diversity and interconnectivities provides a more controlled means of investigating the appropriateness of those techniques. In this work we introduce this problem in terms of machine learning and present a review of the main formalisms that have been used to build artificial GRNs.
Fil: Carballido, Jessica Andrea. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur; Argentina
Materia
Gene Regulatory Networks
Artificial Grns
Bioinformatics
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:11336/43315
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/43315
network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling On Artificial Gene Regulatory NetworksCarballido, Jessica AndreaPonzoni, IgnacioGene Regulatory NetworksArtificial GrnsBioinformaticshttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Gene regulatory networks (GRNs) represent dependencies between genes and their products during protein synthesis at the molecular level. At the present there exist many inference methods that infer GRNs form observed data. However, gene expression data sets have in general considerable noise that make understanding and learning even simple regulatory patterns difficult. Also, there is no well-known method to test the accuracy of inferred GRNs. Given these drawbacks, characterizing the effectiveness of different techniques to uncover gene networks remains a challenge. The development of artificial GRNs with known biological features of expression complexity, diversity and interconnectivities provides a more controlled means of investigating the appropriateness of those techniques. In this work we introduce this problem in terms of machine learning and present a review of the main formalisms that have been used to build artificial GRNs.Fil: Carballido, Jessica Andrea. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur; ArgentinaSociedad Argentina de Informática E Investigación Operativa2008-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/43315Carballido, Jessica Andrea; Ponzoni, Ignacio; On Artificial Gene Regulatory Networks; Sociedad Argentina de Informática E Investigación Operativa; SADIO Electronic Journal of Informatic and Operation Research; 8; 1; 12-2008; 25-341514-6774CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sadio.org.ar/wp-content/uploads/2016/04/EJS_08_Paper_3.pdfinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:07:11Zoai:ri.conicet.gov.ar:11336/43315instacron: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:34982025-09-10 13:07:11.388CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv On Artificial Gene Regulatory Networks
title On Artificial Gene Regulatory Networks
spellingShingle On Artificial Gene Regulatory Networks
Carballido, Jessica Andrea
Gene Regulatory Networks
Artificial Grns
Bioinformatics
title_short On Artificial Gene Regulatory Networks
title_full On Artificial Gene Regulatory Networks
title_fullStr On Artificial Gene Regulatory Networks
title_full_unstemmed On Artificial Gene Regulatory Networks
title_sort On Artificial Gene Regulatory Networks
dc.creator.none.fl_str_mv Carballido, Jessica Andrea
Ponzoni, Ignacio
author Carballido, Jessica Andrea
author_facet Carballido, Jessica Andrea
Ponzoni, Ignacio
author_role author
author2 Ponzoni, Ignacio
author2_role author
dc.subject.none.fl_str_mv Gene Regulatory Networks
Artificial Grns
Bioinformatics
topic Gene Regulatory Networks
Artificial Grns
Bioinformatics
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Gene regulatory networks (GRNs) represent dependencies between genes and their products during protein synthesis at the molecular level. At the present there exist many inference methods that infer GRNs form observed data. However, gene expression data sets have in general considerable noise that make understanding and learning even simple regulatory patterns difficult. Also, there is no well-known method to test the accuracy of inferred GRNs. Given these drawbacks, characterizing the effectiveness of different techniques to uncover gene networks remains a challenge. The development of artificial GRNs with known biological features of expression complexity, diversity and interconnectivities provides a more controlled means of investigating the appropriateness of those techniques. In this work we introduce this problem in terms of machine learning and present a review of the main formalisms that have been used to build artificial GRNs.
Fil: Carballido, Jessica Andrea. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur; Argentina
description Gene regulatory networks (GRNs) represent dependencies between genes and their products during protein synthesis at the molecular level. At the present there exist many inference methods that infer GRNs form observed data. However, gene expression data sets have in general considerable noise that make understanding and learning even simple regulatory patterns difficult. Also, there is no well-known method to test the accuracy of inferred GRNs. Given these drawbacks, characterizing the effectiveness of different techniques to uncover gene networks remains a challenge. The development of artificial GRNs with known biological features of expression complexity, diversity and interconnectivities provides a more controlled means of investigating the appropriateness of those techniques. In this work we introduce this problem in terms of machine learning and present a review of the main formalisms that have been used to build artificial GRNs.
publishDate 2008
dc.date.none.fl_str_mv 2008-12
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/11336/43315
Carballido, Jessica Andrea; Ponzoni, Ignacio; On Artificial Gene Regulatory Networks; Sociedad Argentina de Informática E Investigación Operativa; SADIO Electronic Journal of Informatic and Operation Research; 8; 1; 12-2008; 25-34
1514-6774
CONICET Digital
CONICET
url http://hdl.handle.net/11336/43315
identifier_str_mv Carballido, Jessica Andrea; Ponzoni, Ignacio; On Artificial Gene Regulatory Networks; Sociedad Argentina de Informática E Investigación Operativa; SADIO Electronic Journal of Informatic and Operation Research; 8; 1; 12-2008; 25-34
1514-6774
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sadio.org.ar/wp-content/uploads/2016/04/EJS_08_Paper_3.pdf
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
dc.publisher.none.fl_str_mv Sociedad Argentina de Informática E Investigación Operativa
publisher.none.fl_str_mv Sociedad Argentina de Informática E Investigación Operativa
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_ 1842980315836973056
score 13.004268

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