Boosting reliability when inferring interactions from time-series data in gene regulatory networks
Abstract
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.
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BibTeX
@article{greco2026boosting,
title = {{Boosting reliability when inferring interactions from time-series data in gene regulatory networks}},
author = {Mattia Greco and Federico Ricci-Tersenghi and Olivier C Martin},
journal = {Machine Learning : Science and Technology},
year = {2026},
month = aug,
doi = {10.1088/2632-2153/ae9027},
url = {https://hal.science/hal-05762809v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Boosting reliability when inferring interactions from time-series data in gene regulatory networks
AU - Mattia Greco
AU - Federico Ricci-Tersenghi
AU - Olivier C Martin
PY - 2026
DA - 2026/08/05
JO - Machine Learning : Science and Technology
DO - 10.1088/2632-2153/ae9027
UR - https://hal.science/hal-05762809v1
AB - 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.
SN - 2632-2153
SN - 2632-2153
ER -