Gene Regulatory Network Analysis
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This work introduces novel methods leveraging time-lagged correlations and estimators of mRNA decay rates, leading to significantly improved driver-target inference and a temperature-based rescaling of priors was developed to further enhance prediction reliability.
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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.
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@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 Martin},
journal = {Machine Learning Science and Technology},
year = {2026},
doi = {10.1088/2632-2153/ae9027},
url = {https://doi.org/10.1088/2632-2153/ae9027}
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