Translational biomedical informatics is the application of computational methods to facilitate the translation of basic biomedical science to clinical relevance. An example of this is the multi-step process in which large-scale microarray-based discovery experiments are refined into reliable clinical tests. Unfortunately, the quality of microarray data is a major issue that must be addressed before microarrays can reach their full potential as a clinical molecular profiling tool for personalized and predictive medicine. A new methodology, titled caCORRECT, has been developed to replace or augment existing microarray processing technologies, in order to improve the translation of microarray data to clinical relevance. Results of validation studies show that caCORRECT is able to improve the mean accuracy of microarray gene expression by as much as 60%, depending on the magnitude and size of artifacts on the array surface. As part of a case study to demonstrate the widespread usefulness of caCORRECT, the entire pipeline of biomarker discovery has been executed for the clinical problem of classifying Renal Cell Carcinoma (RCC) specimens into appropriate subtypes. As a result, we have discovered and validated a novel two-gene RT-PCR assay, which has the ability to diagnose between the Clear Cell and Oncocytoma RCC subtypes with near perfect accuracy. As an extension to this work, progress has been made towards a quantitative quantum dot immunohistochemical assay, which is expected to be more clinically viable than a PCR-based test.