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. 2020 Aug 25;10:14149. doi: 10.1038/s41598-020-70941-y

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© The Author(s) 2020

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Workflow of project. siRNA perturbation experiments were carried out systematically per gene, resulting in a change in mRNA level which elicits a regulatory response over time before reaching steady state when gene expression was measured. GRN inference: nested bootstrapping was applied to three inference methods, producing GRNs with an FDR set to 5%. GRN Selection: Estimate goodness of fit with the Balanced Fitting of Errors framework under cross-validation (BFECV) and compare to shuffled topologies (Algorithm 1). Independent Validation: Each inferred GRNs’ ability to predict an independent dataset was evaluated in comparison to a distribution of shuffled topologies. Finally, the overall most predictive GRN was selected. Two novel links were experimentally validated.