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

Gökmen Altay; Zeyneb Kurt; Matthias Dehmer; Frank Emmert-Streib

doi:10.4137/EBO.S13481

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

Evol Bioinform Online. 2014 Feb 6:10:1-9. doi: 10.4137/EBO.S13481. eCollection 2014.

Authors

Gökmen Altay¹, Zeyneb Kurt², Matthias Dehmer³, Frank Emmert-Streib⁴

Affiliations

¹ Biomedical Engineering, Bahçeşehir University, Beşiktaş, Istanbul, Turkey.
² Department of Computer Engineering, Yildiz Technical University, Davutpasa Campus, 34220, Esenler, Istanbul, Turkey.
³ Institute for Bioinformatics and Translational Research, UMIT- The Health and Life Sciences University, Eduard Wallnoefer Zentrum 1, 6060 Hall in Tyrol, Austria.
⁴ 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.

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: R package for the network-based measures; gene regulatory networks; global network-based measures; local network-based measures; metrics for assessing ensemble datasets.