To investigate diabetes, we must combine data sources of basic genetic research, epigenetics, metabolic pathways with data from our clinical studies. Connecting these data of highly heterogeneous types is a challenge, but today this is necessary to answer biomedical questions in all disciplines.
The graphics technology allows a new dimension of data analysis to combat 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 the causes of prediabetes in humans.
Connecting data and applying modern machine learning techniques will help scientists better understand this complex disease and, 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)
#GraphTechnology #DiabetesResearch #MachineLearning
Video credits to Neo4j YouTube channel