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

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Netmes : Assessing gene network inference algorithms by network-based measures. / Altay, Gökmen; Kurt, Zeyneb; Dehmer, Matthias; Emmert-Streib, Frank.

julkaisussa: Evolutionary Bioinformatics, Vuosikerta 10, 07.12.2013.

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Altay, Gökmen ; Kurt, Zeyneb ; Dehmer, Matthias ; Emmert-Streib, Frank. / Netmes : Assessing gene network inference algorithms by network-based measures. Julkaisussa: Evolutionary Bioinformatics. 2013 ; Vuosikerta 10.

Bibtex - Lataa

@article{7a0a38d3643a4fec920da05840e9493b,
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/.",
keywords = "Gene regulatory networks, Global network-based measures, Local network-based measures, Metrics for assessing ensemble datasets, R package for the network-based measures",
author = "G{\"o}kmen Altay and Zeyneb Kurt and Matthias Dehmer and Frank Emmert-Streib",
year = "2013",
month = "12",
day = "7",
doi = "10.4137/EBO.S13481",
language = "English",
volume = "10",
journal = "Evolutionary Bioinformatics",
issn = "1176-9343",
publisher = "Libertas Academica",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Netmes

T2 - Assessing gene network inference algorithms by network-based measures

AU - Altay, Gökmen

AU - Kurt, Zeyneb

AU - Dehmer, Matthias

AU - Emmert-Streib, Frank

PY - 2013/12/7

Y1 - 2013/12/7

N2 - 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/.

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/.

KW - Gene regulatory networks

KW - Global network-based measures

KW - Local network-based measures

KW - Metrics for assessing ensemble datasets

KW - R package for the network-based measures

UR - http://www.scopus.com/inward/record.url?scp=84893596910&partnerID=8YFLogxK

U2 - 10.4137/EBO.S13481

DO - 10.4137/EBO.S13481

M3 - Article

VL - 10

JO - Evolutionary Bioinformatics

JF - Evolutionary Bioinformatics

SN - 1176-9343

ER -