NCATS Biomedical Data Translator

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In the pursuit of biomedical research, scientists often make observations that are difficult to explain. To attempt to explain such an observation, currently it falls to the researcher to manually identify potential connections or ideas by mining data and knowledge across different resources, a task that demands expertise with many different sources of data and relies upon significant recall of insights from previously published research. And what they find or dont find in those endeavors influences their next experimental steps and might in fact influence their working hypothesis. Ideally, one would mine these heterogeneous data systematically to gain insights into the relationship between molecular and cellular processes and the signs and symptoms manifested in diseases. However, there is currently no broadly-used system that gathers information from different sources and uses the aggregated knowledge as building blocks to aid in the construction of a cohesive hypothesis. Overall goal of the Biomedical Data Translator Program: The purpose of the program is to accelerate translational research by developing a system to augment human reasoning in response to biomedical research questions. Translator will serve as a resource for computer-assisted exploration and construction of new research hypotheses by connecting and distilling existing empirical knowledge spanning all types of biomedical data, including environmental, molecular and clinical data. NCATS anticipates that this will be a multi-modal computational tool that leverages multiple types of existing data sources, including objective signs and symptoms of disease, drug effects and intervening types of biological data relevant to understanding pathophysiology. The Biomedical Data Translator, Translator, will be designed to make networks by connecting a variety of data types using both derived knowledge and well-curated data sources and present a user with the most relevant data to help augment their own analysis of translational research problems. In doing so, we will facilitate classification of diseases based on a set of molecular and cellular abnormalities. This would enable recognition of and therapeutic development for conditions with a shared molecular etiology that might manifest differently in the clinic. The Translator must be dynamic and transparent, i.e., able to incorporate new data and information as it becomes available, and able to accommodate a variety of analytical approaches to assist the generation of hypotheses. Translator will be open source and completely publicly available for any user. When fully operational, Translator will be capable of revealing potential relationships across a multitude of disparate data types, from clinical signs and symptoms to cellular and molecular events to environmental exposures. The scope of Translator has been left intentionally vast to leverage biomedical data of all types to help assemble information to address complex questions.