The rapid increase in transcriptome data provides an opportunity to access the complex regulatory mechanisms in cellular systems through gene association network (GAN). Nonetheless, GANs derived from single datasets generally allow us to envisage only one side of the regulatory network, even under the particular condition of study. The circumstance is well demonstrated by inconsistent GANs of individual datasets proposed for similar experimental conditions, which always leads to ambiguous interpretation. Here, pan- and core-gene association networks (pan- and core-GANs), analogous to the pan- and core-genome concepts, are proposed to increase the power of inference through the integration of multiple, diverse datasets. The core-GAN represents the consensus associations of genes that were inferred from all individual networks. On the other hand, the pan-GAN represents the extensive gene-gene associations that occurred in each individual network. The pan- and core-GANs prospects were demonstrated based on three time series microarray datasets in leaves of Arabidopsis thaliana grown under diurnal conditions. We showed the overall performance of pan- and core-GANs was more robust to the number of data points in gene expression data compared to the GANs inferred from individual datasets. In addition, the incorporation of multiple data broadened our understanding of the biological regulatory system. While the pan-GAN enabled us to observe the landscape of gene association system, core-GAN highlighted the basic gene-associations in essence of the regulation regulating starch metabolism in leaves of Arabidopsis.