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/.
authors: Biomedical Engineering, Bahçeşehir University, Beşiktaş, Istanbul, Turkey.
authors: Gökmen
authors: G
authors: Altay
authors: Department of Computer Engineering, Yildiz Technical University, Davutpasa Campus, 34220, Esenler, Istanbul, Turkey.
authors: Zeyneb
authors: Z
authors: Kurt
authors: Institute for Bioinformatics and Translational Research, UMIT- The Health and Life Sciences University, Eduard Wallnoefer Zentrum 1, 6060 Hall in Tyrol, Austria.
authors: Matthias
authors: M
authors: Dehmer
authors: Computational Biology and Machine Learning Laboratory, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK.
authors: Frank
authors: F
authors: Emmert-Streib
keywords: R package for the network-based measures; gene regulatory networks; global network-based measures; local network-based measures; metrics for assessing ensemble datasets