THINK Back: KNowledge-based Interpretation of High Throughput data

Results of high throughput experiments can be challenging to interpret. Current approaches have relied on bulk processing the set of expression levels, in conjunction with easily obtained external evidence, such as co-occurrence. While such techniques can be used to reason probabilistically, they are not designed to shed light on what any individual gene, or a network of genes acting together, may be doing. Our belief is that today we have the information extraction ability and the computational power to perform more sophisticated analyses that consider the individual situation of each gene. The use of such techniques should lead to qualitatively superior results

Simplifying access to a clinical data repository using schema summarization

(CDR) integrates over 25 data sources, and as a result has a schema that is too complex to be directly queried by clinical researchers. Schema summarization uses abstract elements and links to summarize a complex schema and allows users with limited knowledge of the underlying database structure to effectively issue queries to the CDR for clinical and translational research. BACKGROUND Our institution developed a Clinical Data Repository (CDR) in 1998 that now integrates information from over 25 data sources distributed across the Health System. The CDR schema contains over 650 tables and nearly 2200 distinct attributes, and is constantly evolving. Unfortunately, issuing even basic querie