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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Veröffentlicht in:Evolutionary bioinformatics online 2014-01, Vol.10
Hauptverfasser: Gökmen Altay, Zeyneb Kurt, Matthias Dehmer, Frank Emmert-Streib
Format: Artikel
Sprache:eng
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Zusammenfassung: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/ .
ISSN:1176-9343
DOI:10.4137/EBO.S13481