Stem cells have long held tremendous promise as a versatile tool for biomedical research and disease therapy due to their unique ability to self-renew or differentiate into distinct cell lineages. Stem cells play pivotal roles in many stages of organism development, tissue homeostasis, and repair; yet the molecular mechanisms of stem cell self-renewal are only partially understood. More than a dozen signaling pathways are implicated in self-renewal, suggesting regulation by a complex interplay of external signaling cues, transcriptional control, and molecular activities. Most models of self-renewal, however, oversimplify the intricate dynamics associated with maintaining a cell lineage through both symmetric and asymmetric cell divisions. To characterize the molecular foundations of stem cell self-renewal, we developed and applied a Bayesian network machine learning approach to integrate and analyze the largest single collection of high-throughput murine embryonic stem cell data, comprising over 1.5 million data points. Our results confirm roles of genes and proteins known to be involved in self-renewal and predict many novel players. Computational evaluation shows our results are highly accurate and significantly improved over those of prior mammalian data integration methods, which overlooked the functional importance of specific cell types. We are currently pursuing laboratory validations of our novel findings. To provide the broader community access to information and analyses about genes and proteins in the context of self-renewal and pluripotency, we are creating dynamic visualizations of our results that we will package and release in a comprehensive online resource, thus enabling hypothesis creation and refinement of experimental design.