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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Bibliographic Details
Published in:Evolutionary Bioinformatics Vol. 2014; no. 2014; pp. 1 - 9
Main Authors: Altay, Gökmen, Kurt, Zeyneb, Dehmer, Matthias, Emmert-Streib, Frank
Format: Journal Article
Language:English
Published: London, England Libertas Academica 01.01.2014
SAGE Publishing
SAGE Publications
Sage Publications Ltd. (UK)
Sage Publications Ltd
Subjects:
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
Online Access:Get full text
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Description
Summary: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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ACADEMIC EDITOR: Jike Cui, Associate Editor
ISSN:1176-9343
1176-9343
DOI:10.4137/EBO.S13481