High throughput technologies are rapidly changing how clinical research is performed. Clinical researchers now generate gigabytes of data using gene expression profiling, mass spectrometry, and single nucleotide polymorphism analysis. To be understood in a clinically-relevant manner, these data need to be combined with clinical and biological data available in hundreds of public databases. Simultaneous with the exponential growth of available data, the number of available analysis and visualization tools has increased dramatically over the last several years. To facilitate clinical research we provide a workflow-based integration system that aims to tie the best available data analysis methods, software and hardware together with the people who develop them and understand this integration. We provide data management and analytic strategies that combine different high-throughput data modalities with clinical data. An important part of this work is establishing visualization and data exploration techniques which will help biomedical and clinical researchers in the generation and testing of hypotheses. At the same time, novel statistical analysis, clustering, and data-mining approaches are integrated on this platform without being mutually exclusive.