Dr. Philip Cannata 1

Dr. Philip Cannata 2

Dr. Philip Cannata 3

Abstract: 21st century biomedical research is driven by massive amounts of data: automated technologies generate hundreds of gigabytes of DNA sequence information, terabytes of high resolution medical images, and massive arrays of gene expression information on thousands of genes tested in hundreds of independent experiments. Clinical research data is no different: each clinical trial may potentially generate hundreds of data points of thousands of patients over the course of the trial.

This influx of data has enabled a new understanding of disease on its fundamental, molecular basis. Many diseases are now understood as complex interactions between an individual's genes, environment and lifestyle. To harness this new understanding, research and clinical care capabilities (traditionally undertaken as isolated functions) must be bridged to seamlessly integrate laboratory data, biospecimens, medical images and other clinical data. This collaboration between researchers and clinicians will create a continuum between the bench and the bedside-speeding the delivery of new diagnostics and therapies, tailored to specific patients, ultimately improving clinical outcomes.

To realize the promises of this new paradigm of personalized medicine, healthcare and drug discovery organizations must evolve their core processes and IT capabilities to enable broader interoperability among data resources, tools, and infrastructure-both within and across institutions. Answers to these challenges are enabled by the cancer Biomedical Informatics GridT (caBIGT) initiative, overseen by the National Cancer Institute Center for Biomedical Informatics and Information Technology (NCI-CBIIT). caBIGT is a collection of interoperable software tools, standards, databases, and grid-enabled computing infrastructure founded on four central principles: . Open access; anyone-with appropriate permission-may access caBIGT the tools and data. Open development; the entire research community participates in the development, testing, and validation of the tools. Open source; all the tools are available for use and modification. Federation; resources can be controlled locally, or integrated across multiple sites

caBIGT is designed to connect researchers, clinicians, and patients across the continuum of biomedical research-allowing seamless data flow between electronic health records and data sources including genomic, proteomic, imaging, biospecimen, pathology and clinical information, facilitating collaboration across the entire biomedical enterprise.

caBIGT technologies are widely applicable beyond cancer and may be freely adopted, adapted or integrated with other standards-based tools and systems. Guidelines, tools and support infrastructure are in place to facilitate broad integration of caBIGT tools, which are currently being deployed at more than 60 academic medical centers around the United States and are being integrated in the Nationwide Health Information Network as well. For more information on caBIGT, visit http://cabig.cancer.gov/

Semantic Web - caBIG

Dr. Philip Cannata 4

Semantic Web - caBIG

Dr. Philip Cannata 5

Semantic Web

See data “14 Semantic Representation and Query of caBig Data.pdf” on the class website calendar