Advances in DNA microarray technology that enable the simultaneous measurement of thousands of genes in a single experiment has revolutionized current molecular biology. The 21st century is witnessing an explosion in the amount of biological information on normal and disease biological processes. Scalable approaches for the analysis and the organization of this vast amount of information into usable forms is needed. We introduce StepMiner, a tool for the analysis of timecourse microarray data that discovers stepwise changes in the timecourse. StepMiner groups genes whose expressions change at the same direction and at the same time. In addition, we present the use of Boolean implication relationships for mining massive amounts of publicly available gene-expression microarray datasets. The Boolean analysis results are easily understandable and directly interpretable from visual and computational inspection of the heatmap and the scatter plots. We demonstrate how Boolean analysis is used to understand gene regulation, gene function and various human diseases. We have experimentally validated new biological hypothesis that were generated using our Boolean implication network. In particular, we applied our Boolean network toward the identification of genes related to B cell development. We validated these genes by measuring their expression levels on different stages of the mouse B cell development. Two prognostic markers of B cell lymphoma appeared in our prediction that relates clinical information to the biology of B cell development.