Numerous methods have been developed for inferring (reverse engineering) gene regulatory networks from expression data. However, both their absolute and comparative performance remain poorly understood. The aim of this project is to provide benchmarks and tools for rigorous testing of methods for gene network inference.
We have developed novel approaches for the generation of realistic in silico benchmarks and for the identification of systematic errors of network inference algorithms. Our framework is available as an easy-to-use Java tool called GeneNetWeaver (GNW). We are using in vivo microarray compendia side-by-side with synthetic (GNW) data to assess the performance of network inference methods in the DREAM challenge, an annual community-wide network inference challenge.
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Video introduction to GeneNetWeaver (GNW) and in silico benchmark generation and performance profiling of network inference methods.
|An extensible toolkit for modularity detection in networks|
|Java library for simulating stochastic differential equations|
|Observation and interaction in experimental environments|
Numerical Integration of SDEs: A Short Tutorial
Stochastic Simulations for DREAM4