Analysis of multiple types of cancers and thousands of patients to identify potentially potent approved drugs that could be repurposed for a given cancer. This identification is based on multiple sources of information, including large-scale molecular measurements from The Cancer Genome Atlas, the MEDLINE database that includes millions of biomedical scholarly articles, as well as drug, disease, and molecular interactions databases. Performing queries on the Urika system resulted in many repurposing hits that warrant further experimental validation. (Dr. Brady Bernard, Dr. Ilya Shmulevich, Andrea Eakin)

Autism in its various forms is not well understood and is believed to be caused by a multitude of factors whose contribution may be different from case to case. A team lead by Adam Lugowski investigated causes of autism by understanding the clusters of precursors among diagnosed individuals. “We discovered that Urika really shines on a variety of queries as compared to COTS servers. This has allowed us to cluster very large RDF graphs, something not possible on lesser hardware,” said Adam Lugowski. (Adam Lugowski, Dr. John Gilbert, Kevin Deweese)

Improving the speed and accuracy of risk prediction methods, even by a small amount could have a major impact on individual and population health, as well as on health system costs and performance. Dr. Flaxman modeled mortality in the 30 days following a heart attack so that populations at particular risk can be identified. He developed and evaluated an approach to risk prediction particularly suited to big data analysis with the Urika graph appliance. (Dr. Abraham Flaxman)