Netmes: Assessing Gene Network Inference Algorithms by Network-Based Measures

Author:

Altay Gökmen1,Kurt Zeyneb2,Dehmer Matthias3,Emmert-Streib Frank4

Affiliation:

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/ .

Publisher

SAGE Publications

Subject

Computer Science Applications,Genetics,Ecology, Evolution, Behavior and Systematics

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