The amount of available molecular biological data has increased by several orders of magnitude over the past decades, and the quality and accessibility of these data continue to improve exponentially. The ensuing shift toward the "large-p, small-n" paradigm holds great promise for medical discovery, but it also presents unique analytic challenges. Translational medicine is focused on generating clinically relevant conclusions from these large-volume databases, but this goal will be achieved only if the present paradigm shift in data generation is accompanied by a similar paradigm shift in data modeling. Here, we propose five specific conceptual and theoretical changes in data modeling strategies that will facilitate improved translational analysis of large-volume molecular databases.