Netmes: Assessing gene network inference algorithms by network-based measures

Gökmen Altay; Zeyneb Kurt; Matthias Dehmer; Frank Emmert-Streib

doi:10.4137/EBO.S13481

Netmes: Assessing gene network inference algorithms by network-based measures

Gökmen Altay^*
, Zeyneb Kurt
, Matthias Dehmer
, Frank Emmert-Streib

^*Corresponding author for this work

Research output: Contribution to journal › Article › Scientific › peer-review

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/.

Original language	English
Journal	Evolutionary Bioinformatics
Volume	10
DOIs	https://doi.org/10.4137/EBO.S13481
Publication status	Published - 7 Dec 2013
Publication type	A1 Journal article-refereed

Keywords

Gene regulatory networks
Global network-based measures
Local network-based measures
Metrics for assessing ensemble datasets
R package for the network-based measures

ASJC Scopus subject areas

Ecology, Evolution, Behavior and Systematics
Computer Science Applications
Genetics

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title = "Netmes: Assessing gene network inference algorithms by network-based measures",

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/.",

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AU - Dehmer, Matthias

AU - Emmert-Streib, Frank

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AB - 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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KW - Global network-based measures

KW - Local network-based measures

KW - Metrics for assessing ensemble datasets

KW - R package for the network-based measures

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JO - Evolutionary Bioinformatics

JF - Evolutionary Bioinformatics

ER -

Netmes: Assessing gene network inference algorithms by network-based measures

Abstract

Keywords

ASJC Scopus subject areas

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