Genomic approaches such as genome-wide association studies (GWAS), disease genome sequencing projects, and genome-wide expression profiling analyses, in conjunction with classical genetic approaches, can identify human genes that are altered in disease, thereby suggesting a role for the encoded protein (or RNA) in the establishment and/or progression of the disease. However, many technical difficulties challenge our ability to validate the role of these disease-associated genes and gene products. Moreover, many identified genes contain open reading frames (ORFs) that have yet to be annotated, that is, the function (or activity) of the encoded protein is unknown. As a result, translating the genomic information available in public databases into useful tools for understanding and curing disease is a very slow and inefficient process. To overcome these difficulties, we have developed a technology platform, termed the "molecular medicine GPS" (mm-GPS), which is aimed at defining high-quality maps of interaction networks involving disease proteins. These maps are used to identify network dysfunctions in disease cells or models and to develop molecular tools such as RNA interference (RNAi) and small-molecule inhibitors to further characterize the molecular basis of disease. In this article, I review our progress in producing high-quality maps of human protein interaction networks, and I describe how we used this information to identify new factors and pathways that regulate the RNA polymerase II transcription machinery. I also describe how we utilize the mm-GPS platform to guide more efficient efforts leading from disease-associated genes to protein interaction networks to small-molecule inhibitors, and consequently, to accelerate drug and biomarker discovery.