Seminar Abstract

“The Data Driven Challenge of Translational Research”

Translational biomedical research is a data driven challenge. Ability to carry out complex translational studies will rely on effectively tackling challenges posed by information heterogeneity, data coordination and data size. Generating integrated views of biomedical phenomena can involve the need to synthesize information from many high throughput information sources. These sources can include multiple types of high throughput molecular data and multiple imaging modalities. Translational studies often involve coordinated efforts at multiple sites leading to the need to share detailed experimental data and the need to coordinate work. Finally, detailed understanding of biomedical phenomena will increasingly involve the need to analyze very large high resolution spatio-temporal datasets.

We will present an overview of middleware and algorithms designed to respond to challenges posed by heterogeneity, coordination and data size. We will outline the architectural roles played systematic metadata management mechanisms, and by mechanisms that provide an abstract view of combined information stored in filesystems, XML databases and in relational databases. Finally, we will motivate this talk by a variety of examples that arise in exploring the morphological impact of Rb gene knockouts, in grid based CAD image analysis and in caBIG based translational research scenarios.