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Netmes: Assessing Gene Network Inference Algorithms by Network-Based Measures

Gene regulatory network inference (GRNI) algorithms are essential for efficiently utilizing large-scale microarray datasets to elucidate biochemical interactions among molecules in a cell. Recently, the combination of network-based error measures complemented with an ensemble approach became popular...

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Detalles Bibliográficos
Autores principales: Altay, Gökmen, Kurt, Zeyneb, Dehmer, Matthias, Emmert-Streib, Frank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Libertas Academica 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3921134/
https://www.ncbi.nlm.nih.gov/pubmed/24526830
http://dx.doi.org/10.4137/EBO.S13481
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author Altay, Gökmen
Kurt, Zeyneb
Dehmer, Matthias
Emmert-Streib, Frank
author_facet Altay, Gökmen
Kurt, Zeyneb
Dehmer, Matthias
Emmert-Streib, Frank
author_sort Altay, Gökmen
collection PubMed
description Gene regulatory network inference (GRNI) algorithms are essential for efficiently utilizing large-scale microarray datasets to elucidate biochemical interactions among molecules in a cell. Recently, the combination of network-based error measures complemented with an ensemble approach became popular for assessing the inference performance of the GRNI algorithms. For this reason, we developed a software package to facilitate the usage of such metrics. In this paper, we present netmes, an R software package that allows the assessment of GRNI algorithms. The software package netmes is available from the R-Forge web site https://r-forge.r-project.org/projects/netmes/.
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spelling pubmed-39211342014-02-13 Netmes: Assessing Gene Network Inference Algorithms by Network-Based Measures Altay, Gökmen Kurt, Zeyneb Dehmer, Matthias Emmert-Streib, Frank Evol Bioinform Online Original Research Gene regulatory network inference (GRNI) algorithms are essential for efficiently utilizing large-scale microarray datasets to elucidate biochemical interactions among molecules in a cell. Recently, the combination of network-based error measures complemented with an ensemble approach became popular for assessing the inference performance of the GRNI algorithms. For this reason, we developed a software package to facilitate the usage of such metrics. In this paper, we present netmes, an R software package that allows the assessment of GRNI algorithms. The software package netmes is available from the R-Forge web site https://r-forge.r-project.org/projects/netmes/. Libertas Academica 2014-02-06 /pmc/articles/PMC3921134/ /pubmed/24526830 http://dx.doi.org/10.4137/EBO.S13481 Text en © 2014 the author(s), publisher and licensee Libertas Academica Ltd. This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License.
spellingShingle Original Research
Altay, Gökmen
Kurt, Zeyneb
Dehmer, Matthias
Emmert-Streib, Frank
Netmes: Assessing Gene Network Inference Algorithms by Network-Based Measures
title Netmes: Assessing Gene Network Inference Algorithms by Network-Based Measures
title_full Netmes: Assessing Gene Network Inference Algorithms by Network-Based Measures
title_fullStr Netmes: Assessing Gene Network Inference Algorithms by Network-Based Measures
title_full_unstemmed Netmes: Assessing Gene Network Inference Algorithms by Network-Based Measures
title_short Netmes: Assessing Gene Network Inference Algorithms by Network-Based Measures
title_sort netmes: assessing gene network inference algorithms by network-based measures
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3921134/
https://www.ncbi.nlm.nih.gov/pubmed/24526830
http://dx.doi.org/10.4137/EBO.S13481
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