Boosting reliability when inferring interactions from time series data in gene regulatory networks
bioRxiv – February 17, 2025
Source: medRxiv/bioRxiv/arXiv
Summary
Imagine unlocking the secrets of gene interactions with greater accuracy! By refining an existing algorithm, researchers have enhanced the reliability of predicting how genes regulate each other over time. Using innovative methods and simulated data, they achieved impressive improvements in prediction performance, showcasing the power of incorporating prior knowledge into gene studies.
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.