Vân Anh Huynh-Thu

GENIE3

Related paper:
Inferring regulatory networks from expression data using tree-based methods
Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., and Geurts, P.
PLoS ONE, 5(9):e12776, 2010.

Four implementations of GENIE3 are available:
You have also the possibility to run GENIE3 (MATLAB implementation) on the GP-DREAM platform.

Note: All the results presented in the PLoS ONE paper were generated using the MATLAB implementation.

GENIE3 is based on regression trees. To learn these trees, the Python implementation uses the scikit-learn library, the MATLAB and R/C implementations are respectively MATLAB and R wrappers of a C code written by Pierre Geurts, and the R/randomForest implementation uses the randomForest R package.
The R/C implementation is the fastest GENIE3 implementation, and was developed for the SCENIC pipeline to analyze single-cell RNA-seq data (Aibar Santos et al., 2016. Manuscript in preparation.).
The running times of the different GENIE3 implementations are shown below for the DREAM5 networks (in each case, GENIE3 was run using the default parameters). These computing times were measured on a 16GB RAM, Intel Xeon E5520 2.27GHz computer.
GENIE3 running times


GENIE3 was the best performer in two DREAM challenges :

GENIE3 variants

We derived two extensions of GENIE3, for the inference of networks from time series data and systems genetics data (i.e. expression data + genomic data) respectively.

The GENIE3 extension for time series data is described in chapter 8 of my thesis.

The GENIE3 extension for systems genetics data is described in the following book chapter:
Gene regulatory network inference from systems genetics data using tree-based methods
Huynh-Thu V. A., Wehenkel L., and Geurts P.
In: A. de la Fuente (Ed.), Gene Network Inference - Verification of Methods for Systems Genetics Data, Springer, pp. 63-85, 2013.

Python and MATLAB implementations of both GENIE3 extensions are available. Please contact me if you are interested.


Jump3

Related paper:
Combining tree-based and dynamical systems for the inference of gene regulatory networks
Huynh-Thu, V. A. and Sanguinetti, G.
Bioinformatics, 31(10):1614-1622, 2015.