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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Vydáno v:	Evolutionary Bioinformatics Ročník 2014; číslo 2014; s. 1 - 9
Hlavní autoři:	Altay, Gökmen, Kurt, Zeyneb, Dehmer, Matthias, Emmert-Streib, Frank
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London, England Libertas Academica 01.01.2014 SAGE Publishing SAGE Publications Sage Publications Ltd. (UK) Sage Publications Ltd
Témata:	Algorithms Analysis Assessments Biochemistry Bioinformatics Genes Inference Networks Original Research Software packages metrics for assessing ensemble datasets R package for the network-based measures gene regulatory networks global network-based measures local network-based measures
ISSN:	1176-9343, 1176-9343
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Abstract	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/.
AbstractList	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 KEYWORDS: global network-based measures, local network-based measures, metrics for assessing ensemble datasets, R package for the network-based measures, gene regulatory networks 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/. 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 website https://r-forge.r-project.org/projects/netmes/. 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/.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/.
Audience	Academic
Author	Matthias Dehmer Frank Emmert-Streib Zeyneb Kurt Gökmen Altay
AuthorAffiliation	1 Biomedical Engineering, Bahçeşehir University, Beşiktaş, Istanbul, Turkey 2 Department of Computer Engineering, Yildiz Technical University, Davutpasa Campus, 34220, Esenler, Istanbul, Turkey 3 Institute for Bioinformatics and Translational Research, UMIT- The Health and Life Sciences University, Eduard Wallnoefer Zentrum 1, 6060 Hall in Tyrol, Austria 4 Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast, UK
AuthorAffiliation_xml	– name: 4 Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast, UK – name: 2 Department of Computer Engineering, Yildiz Technical University, Davutpasa Campus, 34220, Esenler, Istanbul, Turkey – name: 1 Biomedical Engineering, Bahçeşehir University, Beşiktaş, Istanbul, Turkey – name: 3 Institute for Bioinformatics and Translational Research, UMIT- The Health and Life Sciences University, Eduard Wallnoefer Zentrum 1, 6060 Hall in Tyrol, Austria
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Cites_doi	10.1126/science.298.5594.824 10.1186/1471-2105-9-461 10.1038/ng881 10.1093/nar/gkh1009 10.1073/pnas.0913357107 10.1049/iet-syb:20060079 10.1049/iet-syb.2010.0028 10.1155/2009/617281 10.1093/bioinformatics/btg313 10.1371/journal.pone.0029279 10.1186/1471-2105-7-43 10.1093/bioinformatics/btq259
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Keywords	metrics for assessing ensemble datasets R package for the network-based measures gene regulatory networks global network-based measures local network-based measures
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Title	Netmes: Assessing Gene Network Inference Algorithms by Network-Based Measures
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