Transcriptional regulatory networks (TRNs) can be developed by computational approaches that infer regulator-target gene interactions from transcriptional assays. Successful algorithms that generate predictive, accurate TRNs enable the identification of regulator-target relationships in conditions where experimentally determining regulatory interactions is a challenge. Improving the ability of TRNs to successfully predict known regulator-target relationships in model species will enhance confidence in applying these approaches to determine regulator-target interactions in non-model species where experimental validation is challenging. Many transcriptional profiling experiments are performed across multiple time points; therefore we sought to improve regulator-target predictions by adjusting how time is incorporated into the network. We created ExRANGES, which incorporates Expression in a Rate-Normalized GEne Specific manner that adjusts how expression data is provided to the network algorithm. We tested this on a two different network construction approaches and found that ExRANGES prioritizes targets differently than traditional expression and improves the ability of these networks to accurately predict known regulator targets. ExRANGES improved the ability to correctly identify targets of transcription factors in large data sets in four different model systems: mouse, human, Arabidopsis, and yeast. Finally, we examined the performance of ExRANGES on a small data set from field-grown Oryza sativa and found that it also improved the ability to identify known targets even with a limited data set.