On Artificial Gene Regulatory Networks

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 ma...

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Detalles Bibliográficos
Autores principales: Carballido, Jessica A., Ponzoni, Ignacio
Formato: Articulo
Lenguaje:Inglés
Publicado: 2008
Materias:
Ciencias Informáticas
Gene Regulatory Networks
Artificial GRNs
Bioinformatics
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/135406
https://publicaciones.sadio.org.ar/index.php/EJS/article/view/97
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-135406
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Gene Regulatory Networks
Artificial GRNs
Bioinformatics
spellingShingle Ciencias Informáticas
Gene Regulatory Networks
Artificial GRNs
Bioinformatics
Carballido, Jessica A.
Ponzoni, Ignacio
On Artificial Gene Regulatory Networks
topic_facet Ciencias Informáticas
Gene Regulatory Networks
Artificial GRNs
Bioinformatics
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
format Articulo
Articulo
author Carballido, Jessica A.
Ponzoni, Ignacio
author_facet Carballido, Jessica A.
Ponzoni, Ignacio
author_sort Carballido, Jessica A.
title On Artificial Gene Regulatory Networks
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
publishDate 2008
url http://sedici.unlp.edu.ar/handle/10915/135406
https://publicaciones.sadio.org.ar/index.php/EJS/article/view/97
work_keys_str_mv AT carballidojessicaa onartificialgeneregulatorynetworks
AT ponzoniignacio onartificialgeneregulatorynetworks
bdutipo_str Repositorios
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