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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Libertas Academica
2014
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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/. |
format | Online Article Text |
id | pubmed-3921134 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Libertas Academica |
record_format | MEDLINE/PubMed |
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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