Boosting Reliability when Inferring Interactions from Time-Series Data in Gene Regulatory Networks

In the context of the dynGENIE3 (Anh Huynh-Thu and Geurts 2018 Sci. Rep. 8 3384) approach for inferring regulatory network interactions from time-series data, we show that it is possible to modify that algorithm to significantly enhance its prediction reliability. To quantify the level of reliability, we used ground-zero truths based on simulated datasets generated by the GeneNetWeaver (Schaffter et al 2011 Bioinformatics 27 2263–70) tool. Our work introduces novel methods leveraging time-lagged correlations and estimators of mRNA decay rates, leading to significantly improved driver-target inference. Additionally, a temperature-based rescaling of priors was developed to further enhance prediction reliability. Results demonstrate substantial improvements in performance with a particularly notable increase in area under the precision recall curve scores. These advances underscore the possible gains resulting from incorporating priors into gene regulatory network inference.