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 |
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Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
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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/ . |
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ISSN: | 1176-9343 |
DOI: | 10.4137/EBO.S13481 |