data of other disciplines, such as genomics or proteomics. In the human body, everything is connected in metabolic pathways; a gene encodes a protein that is active in a metabolic pathway and metabolises a metabolite, which in turn is able to regulate another gene. In a way, our metabolism is a network of thousands of components that are connected with each other, which is a graph data model.
Link diabetes research with Alzheimer’s That’s why it’s so important to be able to uncover these connections and to create a new layer of analysis on top of this data, so we use technology from the graph database world called Neo4j. The great thing about Neo4j is that it has a visual interface we can use for queries and experimentation. We are using it to deepen our ‘map’ of diabetes – to uncover hidden relationships and pursue the
“The healthcare sector is increasingly turning away from general blockbuster drugs and moving to individualised treatment”
resulting new questions. For example, we do a lot of
important research on animal models to study processes and then compare them to humans, so there is a lot of animal data from mice and pigs. This can generate a hypothesis we want to pursue – for example, ‘In the pig model is the prediabetes type X due to causes A and B?’ Is this regulated similarly? Are there similar processes? We think we can link the molecular human data from the basic research with the
highly standardised animal model data. In a graph representation, abnormalities, patterns or connections can then be recognised, which will then lead to further research questions. In the long term, it would also be interesting if data from diabetes research could also be used for other areas, such as cancer or Alzheimer’s research in order to uncover possible connections. This isn’t the only advanced
technology we see as being useful. For example, we will definitely use machine learning techniques with graph software to identify unknown patterns – for example, to try to identify new subtypes of diabetes we find discussed in the literature. Another example is Natural Language Processing – we’d like to build a system that automatically reads scientific texts from literature databases, analyses them and together with our research data generates
hypotheses that can be evaluated by DZD scientists. Also conceivable: predictive models that can prescribe the course of the disease to a certain degree of probability. This is all coming, and we
are certain that our data management and analysis approach will take us to the next level in precision medicine, prevention and treatment of diabetes. In general, technology and data absolutely have a central role to meeting the Grand Challenges that the UK wants to take on.
Dr Alexander Jarasch, is head of data and knowledge management at Munich’s head-office of the German Centre for Diabetes Research, the DZD (Das Deutsche Zentrum für Diabetesforschung)
Professor Dr Martin Hrabě de Angelis is speaker and member of the board of the DZD, director of the Institute of Experimental Genetics, Helmholtz Zentrum Munich; and Chair of Experimental Genetics, School of Life Science Weihenstephan, Technical University of Munich, Germany
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