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 diagnostic tests.

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. The FDA has completed phase-I of the MicroArray Quality Control (MAQC) project, and is currently developing guidelines and standards on microarray data reporting, quality control, and data analysis [1]. The current status of microarray quality control (QC) and noise reduction however, is still a controversial collection of tools and methods. While competing model-based tools such as dChip [2, 3], MAS5.0 [4], RMA [5, 6], and PLIER [7] have been developed to improve the quality of microarray gene expression data, these tools fall short in two important areas (1) they do not incorporate adequate spatial information into the outlier detection methods and (2) they do not incorporate outlier information into their normalization routines. The methodology discussed in this dissertation, called caCORRECT, addresses these deficiencies and seeks to replace or augment existing technologies in order to improve the translation of microarray data to clinical relevance [8-12].

As a case study to validate and demonstrate the usefulness of caCORRECT, the entire workflow of biomarker discovery was executed for the clinical problem of classifying Renal Cell Carcinoma (RCC) specimens into appropriate subtypes [13, 14]. Two biomarkers are discovered, NNMT and PRKAB1, which are able to separate the chromophobe and clear cell subtypes of RCC with perfect accuracy for all of the samples tested. To translate this discovery into a clinically relevant test, improvements are made to the reliability of quantum dot based immunohistochemistry [15, 16].