caOBR: A tool for indexing cancer informatics resources within the NCBO Resource Index

Nathan Baker

Washington University in St. Louis

Cancer nanotechnology is a new approach in the fight against cancer. Nanoparticle vectors have unique physical properties that allow them to be used for early detection, diagnosis, and therapy in almost every type of cancer. This highly interdisciplinary field draws from areas such as materials science, physics, chemistry, molecular biology, and medicine.

As a relatively new field, cancer nanotechnology presents a unique opportunity for informatics-driven approaches to accelerate discovery and translation. Nanoparticles consist of 1) a core constituent material, 2) targeting/imaging/therapeutic payloads, and 3) biological surface modifiers; and therefore are inherently combinatorial in nature. This combinatorial nature creates a huge space of possible nanoparticle compositions exceeding any individual’s cognitive capacity. Informatics tools are a natural fit for addressing this need by providing decision support in the analysis and design of nanoparticles. We are already participating in the design, implementation and curation of the first informatics resource for cancer nanotechnology to explicitly address this nanotechnology informatics need: the caBIG nanoparticle database repository known as caNanoLab. In this DBP, we aim to create a powerful new toolset for information retrieval for cancer nanotechnology-related information using biomedical ontologies.

This talk will discuss our initial work towards integrating new sources of nanoparticle data into NCBO Resource Index, formerly referred to as the Open Biomedical Resources.  While the NCBO Resource Index supports several existing data sources, none of these contain the detailed nanoparticle information needed by researchers in the cancer nanotechnology field. We propose to provide such information by integrating caNanoLab, a primary source of nanoparticle data for cancer research, with the NCBO Resource Index service. This integration will also lay the groundwork for incorporating a much wider range of biological information from a multitude of caBIG applications via queries over the caGrid.