Journal Article

·2014 OPEN ACCESS

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

Gökmen Altay YTU , Zeyneb Kurt YTU , Matthias Dehmer , Frank Emmert‐Streib

Evolutionary Bioinformatics

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

Inference Computer science Gene regulatory network Algorithm Data mining Data science Bioinformatics Artificial intelligence Gene Biology

Subject Areas

Gene Regulatory Network Analysis ·Molecular Biology ·Life Sciences
Bioinformatics and Genomic Networks ·Molecular Biology ·Life Sciences
Evolution and Genetic Dynamics ·Genetics ·Life Sciences

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