DZDconnect: mit vernetzten Daten gegen Diabetes

Abstract

Data in translational healthcare research is complex and highly connected. Information on wide-spread diseases like diabetes and cancer is extensive, heterogeneous and rapidly growing. Data are available at various locations and are neither interconnectable with other data sources nor searchable. Consequently, it is difficult for researchers to access data and to cope with the amount of literature. Collecting data and knowledge is still done manually by comparing data tables. However, a flexible and efficient approach to processing biomedical data is offered by graph databases. Based on the open source Neo4j graph database, the German Center for Diabetes Research (DZD) developed DZDconnect—a knowledge graph that links data from basic research and clinical studies across sites, disciplines and species with external knowledge. DZDconnect collects, structures, interconnects and makes available various data and information on wide-spread diseases and its long-term complications. Information from well-established databases is connected on the metadata level, raw data level as well as on the insight level. In addition, in-house data from translational research can be integrated. The enabling technology is a flexible and scalable graph database. DZDconnect thus bridges the gap between healthcare research and state-of-the-art information technology and helps to make disease research faster and more efficient. With DZDconnect scientists can quickly and efficiently generate hypotheses regarding the underlying mechanisms of these diseases and how to intervene medically. DZDconnect is developed as an open-source project.