by Roberto V. Zicari, ODBMS Industry Watch on February 4, 2019

“The challenge is that we have to combine lots of different types of data, simultaneously, depending on genetics, epigenetics, different subject matter areas such as lipidomics, metabolomics, the lifestyle and behaviour of the patient and looking at people in different cultures and environments. The variety of data we need to analyse is a major challenge, which is why from a data perspective we use graph. It is here we can make the links to answer biomedical queries.” –Alexander Jarasch.

I have interviewed Alexander Jarasch, head of data and knowledge management at the German Center for Diabetes Research (DZD). We discussed what are the main challenges in trying to understand more about diabetes, and how diabetes researchers are using graph database technology in order to create knowledge graphs and find hidden connections in medical data. – RVZ

Q1. You are the head of data and knowledge management at the German Center for Diabetes Research (DZD). What are your main tasks?

Alexander Jarasch: There are several responsibilities that my team fulfils within DZD, – these include IT infrastructure which can encompass databases, data transfer services, data management and knowledge management as a second part of our remit.

Q2. Diabetes is one of the most widespread diseases worldwide. What are the main challenges in trying to understand more about diabetes?

Alexander Jarasch: Diabetes is a metabolic disease, and a complex area to understand. It is not yet obvious what causes type 2 diabetes, but it is clearly linked to obesity. Here, we try to understand the molecular mechanisms, where diabetes starts and how we can try and prevent it. The challenge is that we have to combine lots of different types of data, simultaneously, depending on genetics, epigenetics, different subject matter areas such as lipidomics, metabolomics, the lifestyle and behaviour of the patient and looking at people in different cultures and environments.

All these dependencies are connected to each other. Metabolism is connected to the environment, genetics, epigenetics and so forth. The big challenge is to see this not just from one perspective, but from as many perspectives at the same time as we can get.

From a data management point of view it is not easy bringing all this patient-related data together with basic research data, and then to combine it with publicly available data, all held in disparate data stores and sources. We need to bring this heterogenous data together and connect it in a very clear way.

Q3. How is the status of research in treating and preventing the disease?

Alexander Jarasch: Diabetes is not currently curable. We have to distinguish between type 1 and type 2 diabetes. Preventing type 1 is not relevant, as it is genetic and one inherits it. Preventing type 2 is very complicated. Obviously it is suggested that patients lead a healthier life, play more sport and drink less alcohol. But some patients don’t respond to lifestyle interventions.

The research itself is very complex and diverse. You can look at it from the patient side, the basic research side or the animal model side. Preventing diabetes is a complicated field and the research is ongoing. There is no clear outcome for the patient at present.

Read more at: http://www.odbms.org/blog/2019/02/on-gaining-knowledge-of-diabetes-using-graphs-interview-with-alexander-jarasch/

 

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