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GENIE3 specifications


Unique identifier OMICS_01683
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages MATLAB, R
Computer skills Advanced
Stability Stable
Maintained Yes


No version available


  • person_outline Vân Anh Huynh-Thu

Publication for GENIE3

GENIE3 citations


BTNET : boosted tree based gene regulatory network inference algorithm using time course measurement data

BMC Syst Biol
PMCID: 5861501
PMID: 29560827
DOI: 10.1186/s12918-018-0547-0

[…] f regulatory delay induced by noisy environment, DDGni [] captures the dynamic delay by applying the gapped local alignment algorithm.One of the state-of-the-art methods used in model-free methods is GENIE3-time, a time-lagged version of GENIE3 [, ]. Basically, GENIE3 applies a tree-based ensemble method to compute scores of regulatory interactions. GENIE3 won both the DERAM4 in-silico multi-facto […]


dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data

Sci Rep
PMCID: 5821733
PMID: 29467401
DOI: 10.1038/s41598-018-21715-0

[…] rpretable than model-free methods. Most importantly, model-based methods can be used for simulating and predicting the dynamical system behaviour under perturbations.In our previous work, we proposed GENIE3, a model-free method that infers networks from steady-state expression data. This method exploits variable importance scores derived from Random forests to identify the regulators of each targe […]


GTL1 and DF1 regulate root hair growth through transcriptional repression of ROOT HAIR DEFECTIVE 6 LIKE 4 in Arabidopsis

PMCID: 5818008
PMID: 29439132
DOI: 10.1242/dev.159707

[…] Gene network inference with ensemble of trees 3 (GENIE3) () was used to predict the downstream targets of GTL1 and RSL4. GENIE3 uses regression tree inference to build the GRN that best fits the experimental data. Microarray data from gtl1-1, df1-1, […]


Reverse engineering of gene networks for regulating early blood development from single cell measurements

BMC Med Genomics
PMCID: 5751697
PMID: 29297370
DOI: 10.1186/s12920-017-0312-z

[…] It is fully consistent with the network predicted in []. To make predictions about the potential regulations among these nine genes, we extend the network by including more regulations. We apply the GENIE3 algorithm to the raw dCt data of these nine genes only. According to the calculated weight of the target edges, we select the highest weight of 27 one-way regulations. Since there are a few two […]


Prophetic Granger Causality to infer gene regulatory networks

PLoS One
PMCID: 5718405
PMID: 29211761
DOI: 10.1371/journal.pone.0170340

[…] iewed as states in a discrete Markov process since the interval between time points t and (t + 1) could be different from the interval between (t + 1) and (t + 2).As we demonstrate with the augmented GENIE3 approach, the use of both past and future time points in deriving causal links can be extended to other methods, such as non-linear regression or mutual information networks. The prophetic augm […]


Exploring candidate biological functions by Boolean Function Networks for Saccharomyces cerevisiae

PLoS One
PMCID: 5628832
PMID: 28981547
DOI: 10.1371/journal.pone.0185475

[…] sed. For example, the method of GeneNet [] converts correlation network into partial correlation graphs and further establishes partial ordering of nodes based on the covariance matrix. The method of GENIE3 [] solves a regression problem for every gene using tree-based ensemble methods. The method of generalized local learning (GLL) performs local learning and feature selection in graphs [, ].Base […]


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GENIE3 institution(s)
Department of Electrical Engineering and Computer Science, Systems and Modeling, University of Liège, Liège, Belgium

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