Genome-wide messenger RNA profiling provides a snapshot of the global state of the cell under different experimental conditions such as diseased versus normal cellular states. However, because measurements are in the form of quantitative changes in messenger RNA levels, such experimental data does not provide direct understanding of the regulatory molecular mechanisms responsible for the observed changes. Identifying potential cell signaling regulatory mechanisms responsible for changes in gene expression under different experimental conditions or in different tissues has been the focus of many computational systems biology studies. Most popular approaches include promoter analysis, gene ontology, or pathway enrichment analysis, as well as reverse engineering of networks from messenger RNA expression data. Here we present a rational approach for identifying and ranking protein kinases that are likely responsible for observed changes in gene expression. By combining promoter analysis; data from various chromatin immunoprecipitation studies such as chromatin immunoprecipitation sequencing, chromatin immunoprecipitation coupled with paired-end ditag, and chromatin immunoprecipitation-on-chip; protein-protein interactions; and kinase-protein phosphorylation reactions collected from the literature, we can identify and rank candidate protein kinases for knock-down, or other types of functional validations, based on genome-wide changes in gene expression. We describe how protein kinase candidate identification and ranking can be made robust by cross-validation with phosphoproteomics data as well as through a literature-based text-mining approach. In conclusion, data integration can produce robust candidate rankings for understanding cell regulation through identification of protein kinases responsible for gene expression changes, and thus rapidly advancing drug target discovery and unraveling drug mechanisms of action.