Semi-parametric approach for the inference of gene regulatory networks from time series of expression data.
The dynGENIE3 method is described in the following paper (available here):
dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data
Huynh-Thu, V. A. and Geurts, P.
Scientific Reports, 8:3384, 2018.
Three implementations of dynGENIE3 are available: Python, MATLAB and R. Each folder contains a PDF file with a step-by-step tutorial showing how to run the code.
Note: All the results presented in the paper were generated using the Python implementation.
dynGENIE3 is based on regression trees. To learn these trees, the Python implementation uses the scikit-learn library, and the MATLAB and R implementations are respectively MATLAB and R wrappers of a C code written by Pierre Geurts.