In order to investigate diabetes, we have to combine basic research data sources from genetics, epigenetics, metabolic pathways with data from our clinical studies. Connecting these highly heterogenous types data is a challenge, but today this is necessary to answer biomedical questions across disciplines.
Graph technology enables a new dimension of data analyses to fight diabetes by connecting data from various species, locations and disciplines. Here we present a use case to study prediabetes where our graph includes data from animal models, genetics, metabolomics and literature to deduce causes of prediabetes in human.
Connecting data and applying modern machine learning techniques will help scientists getting closer to understand this complex disease and will hopefully help to care for patients in the future.
Dr. Alexander Jarasch is the Head of Data and Knowledge Management at The German Center for Diabetes Research (DZD)
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