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The Scientific Information Landscape and Applications to Translational Research

Creating a vision


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Investments made in biomedical and translational research have led to advancements in providing insights into the mechanisms and disease relevant markers that are implicated in human pathologies. This  advancement has led to innovative approaches in designing basic research and clinical trial strategies that are more effective in translating the discoveries made at the bench to treatments delivered to the bedside. In order for these advancements to be truly effective, traditional and coveted research silos should be broken down, making these discoveries more available so they may be shared and leveraged across the biomedical research community. This is expected to require a more open and collaborative environment in which privately and publicly funded researchers, primary investigators and clinicians work together through a knowledge exchange where they share and leverage their collective discoveries and insights in designing new, novel and innovative approaches to treating human diseases.

With the advent of this investment in biomedical research, technologies and collaborative networks comes an explosion of data; data derived from many different sources, structured in distinctly different formats, analyzed using different tools and interpreted from different perspectives. A translational research informatics (TRI) strategy can be used to help overcome many of these challenges to help realize the potential this information brings to the advancement of new, novel, safe, effective and innovative treatments for human diseases.

In this paper, we have set the context for an anticipated future vision of the postcommoditized genomic research environment, where TRI solutions can be implemented to operate to succeed. We have outlined an approach to acquiring and storing biomedical data from several sources, implementing and enforcing data standards and interoperability, integrating the data and anchoring them around exhaustive controlled vocabularies and ontologies, creating and making available tools for analysis of heterogeneous data sets and providing an appropriate supportive technology base to enable these functions. Implementing these changes can help determine the smooth operation of a TRI solution, fulfilling the scientific needs of the community, increasing the appeal of the platform and catalyzing adoption and collaboration. Our paper describes specific execution measures involving strategy formulation, careful planning, multithreaded execution and an ongoing change management as a means of translating ideas to action, thereby increasing a TRI solution’s usage and making it an invaluable collaborative resource. We believe that the approach we lay out can be widely adaptable across commercial industries, academia and governmental agencies such as the National Institutes of Health, Food and Drug Administration, Centers for Disease Control and Prevention, Department of Energy, Environmental Protection Agency, as well as others.

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