Learning or inferring networks of genomic regulation specific to a cellular state, such as a subtype of tumor, can yield insight above and beyond that resulting from network-learning techniques which do not acknowledge the adaptive nature of the cellular system. In this study we show that Cellular Context Mining, which is based on a mathematical model of contextual genomic regulation, produces gene regulatory networks (GRNs) from steady-state expression microarray data which are specific to the varying cellular contexts hidden in the data; we show that these GRNs not only model gene interactions, but that they are also readily annotated with context-specific genomic information. We propose that these context-specific GRNs provide advantages over other techniques, such as clustering and Bayesian networks, when applied to gene expression data of cancer patients.