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Netmes: assessing gene network inference algorithms by network-based measures.

Altay G, Kurt Z, Dehmer M, Emmert-Streib F - Evol. Bioinform. Online (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Engineering, Bahçeşehir University, Beşiktaş, Istanbul, Turkey.

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/.

No MeSH data available.


Total in-degree and out-degree of edges (Ds) in the (A) synthetic and (B) real biological networks.
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Related In: Results  -  Collection


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f4-ebo-10-2014-001: Total in-degree and out-degree of edges (Ds) in the (A) synthetic and (B) real biological networks.

Mentions: Another network-based measure calculates the variable Ds, which is the sum of the out-degree of node i plus the in-degree of node j that shows the effect of the neighbor edges on the inferability of each edge in the network, similar to Figure 3 in Ref. 1. An example of this can be seen in Figure 4(a), generated with the following function that results also in a saved figure with the name Ds.eps:


Netmes: assessing gene network inference algorithms by network-based measures.

Altay G, Kurt Z, Dehmer M, Emmert-Streib F - Evol. Bioinform. Online (2014)

Total in-degree and out-degree of edges (Ds) in the (A) synthetic and (B) real biological networks.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3921134&req=5

f4-ebo-10-2014-001: Total in-degree and out-degree of edges (Ds) in the (A) synthetic and (B) real biological networks.
Mentions: Another network-based measure calculates the variable Ds, which is the sum of the out-degree of node i plus the in-degree of node j that shows the effect of the neighbor edges on the inferability of each edge in the network, similar to Figure 3 in Ref. 1. An example of this can be seen in Figure 4(a), generated with the following function that results also in a saved figure with the name Ds.eps:

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Engineering, Bahçeşehir University, Beşiktaş, Istanbul, Turkey.

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/.

No MeSH data available.