Boosting reliability when inferring interactions from time-series data in gene regulatory networks
Year: 2026
Type: article
Abstract: In the context of the dynGENIE3 [13] 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 [22] 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 AUPRC scores. These advances underscore the possible gains resulting from incorporating priors into gene regulatory network inference.
Authors: Mattia Greco, Federico Ricci-Tersenghi, Olivier C Martin
Institutions: Centre National de la Recherche Scientifique, Université Paris Sciences et Lettres, Istituto Nazionale di Fisica Nucleare, Sezione di Roma I, Sapienza University of Rome, Université Paris Cité, Université Paris-Saclay, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Institut des Sciences des Plantes de Paris Saclay
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,000
OpenAlex ID: W4407813316