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bioRxiv · 10.1101/2025.02.17.638617

Boosting reliability when inferring interactions from time series data in gene regulatory networks.

Greco, M.·Ricci-Tersenghi, F.·Martin, O. C.
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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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Greco, M., Ricci-Tersenghi, F., Martin, O. C.. 2025-02-21. Boosting reliability when inferring interactions from time series data in gene regulatory networks.. https://doi.org/10.1101/2025.02.17.638617

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