Gene expression signatures consisting of tens to hundreds of genes have been found to be informative for different biological states. Recently, many computational methods have been proposed for biological interpretation of such signatures. However, there is a lack of methods for identifying cell signaling pathways whose deregulation results in an observed expression signature. We present a strategy for identifying such signaling pathways and evaluate the strategy using six human and mouse gene expression signatures.