De-novo reverse-engineering of genome-scale regulatory networks is an increasingly important objective for biological and translational research. While many methods have been recently developed for this task, their absolute and relative performance remains poorly understood. The present study conducts a rigorous performance assessment of 32 computational methods/variants for de-novo reverse-engineering of genome-scale regulatory networks by benchmarking these methods in 15 high-quality datasets and gold-standards of experimentally verified mechanistic knowledge. The results of this study show that some methods need to be substantially improved upon, while others should be used routinely. Our results also demonstrate that several univariate methods provide a "gatekeeper" performance threshold that should be applied when method developers assess the performance of their novel multivariate algorithms. Finally, the results of this study can be used to show practical utility and to establish guidelines for everyday use of reverse-engineering algorithms, aiming towards creation of automated data-analysis protocols and software systems.
Bibliographical noteFunding Information:
Alexander Statnikov and Constantin F. Aliferis are acknowledging support from grants R56 LM007948-04A1 from the National Library of Medicine, National Institute of Health and 1UL1RR029893 from the National Center for Research Resources, National Institutes of Health . Varun Narendra was supported by the New York University Medical Science Training Program . We would also like to acknowledge Dimitris Anastassiou and John Watkinson for modifying the SA-CLR algorithm for our experiments and for running it on the Columbia University high performance computing facility; Boris Hayete for providing us with details on reproducing results of  ; Robert Castelo for providing us with details on reproducing results of  ; Peng Qiu for providing us with codes for fast computation of pairwise mutual information as in  ; and Thomas Schaffter and Daniel Marbach for assistance with GeneNetWeaver gene network simulator.
- Computational methods
- Gene expression microarray analysis
- Regulatory network de-novo reverse-engineering