S. Aibar, Scenic: single-cell regulatory network inference and clusting, Nat. Methods, vol.14, issue.11, p.1083, 2017.

J. Davis and M. Goadrich, The relationship between precision-recall and roc curves in Proceedings of the 23rd international conference on Machine learning, pp.233-240, 2006.

J. J. Faith, Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles, PLoS Biol, vol.5, issue.1, p.8, 2007.

T. Fawcett, An introduction to roc analysis, Pattern recognition letters, vol.27, issue.8, pp.861-874, 2006.

K. Glass, High performance computing of gene regulatory networks using a message-passing model, 2015 IEEE High Performance Extreme Computing Conference (HPEC), pp.1-6, 2015.

J. H. Friedman, Stochastic gradient boosting, Comput. Stat. Data Anal, vol.38, issue.4, pp.367-378, 2002.

K. Glass, High performance computing of gene regulatory networks using a message-passing model, 2015 IEEE High Performance Extreme Computing Conference (HPEC), pp.1-6, 2015.

A. Haury, Tigress: trustful inference of gene regulation using stability selection, BMC Syst. Biol, vol.6, issue.1, p.145, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00694218

V. A. Huynh-thu, Inferring Regulatory Networks from expression data Using tree-based methods, PLoS ONE, vol.5, issue.9, 2010.

A. Irrthum, Inferring regulatory networks from expression data using tree-based methods, PloS One, vol.5, issue.9, p.12776, 2010.

E. Jones, SciPy: Open source scientific tools for Python, 2001.

S. Jung, Evaluation of data discretization methods to derive platform independent isoform expression signatures for multi-class tumor subtyping, BMC genomics, vol.16, issue.11, 2015.

M. I. Love, Moderated estimation of fold change and dispersion for RNA-seq data with deseq2, 2014.

, Genome Biol, vol.15, issue.12, p.550

D. Marbach, Wisdom of crowds for robust gene network inference, Nat. Methods, vol.9, issue.8, p.796, 2012.

. W. Mckinney, Data structures for statistical computing in Python, Proceedings of the 9th Python in Science Conference, pp.51-56, 2010.

. T. Oliphant, A guide to NumPy, 2006.

R. A. Olshen, B. Rajaratnam, and F. Pedregosa, Successive normalization of rectangular arrays, Ann. Stat, vol.38, issue.3, pp.2825-2830, 2010.

S. Peignier, Data-driven gene regulatory network inference based on classification algorithms, 31st International Conference on Tools with Artificial Intelligence, pp.1-8, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02361914

G. Sanguinetti and V. A. Huynh-thu, Gene regulatory network inference: an introductory survey, Gene Regulatory Networks, pp.1-23, 2019.

P. Virtanen, SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nature Methods, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02520043

B. Zhang and S. Horvath, A general framework for weighted gene co-expression network analysis, Stat. Appl. Genet. Mol. Biol, vol.4, issue.1, 2005.