LABORATORY INFORMATICS
to discoveries in medical research Dr Alexander Jarasch and Professor Martin Hrabě de Angelis explain that novel research methods produce tremendous amounts of data that cannot be analysed with classic analysis tools. Scientists need to look for new approaches, such as graph technology
How connecting data may lead
promising avenue is to use big data levels of data, so as to combine and better connect data to go further.
With its Grand Challenges initiative, the UK has set a target of using data, AI and innovation to transform the prevention, early diagnosis and treatment of diseases like cancer, diabetes, heart disease and dementia to prevent a potential 25,000 deaths a year.
Similar ambitions exist in
many other countries – this is also a major question for all researchers worldwide at the moment, including in Germany. If we are serious about dealing with the challenges they represent for patients and society and healthcare systems as a whole, we need to study these diseases in much more depth in order to provide novel methods for prevention and treatment of diabetes. We believe these new
technologies will be crucial in gaining new insights into the workings and causes of these chronic conditions and diseases. The problem faced by everyone trying to do this is that the analysis methods we have been relying on may have reached their limits due to the vast amount of data produced by novel research methods (e.g. omics). The really
Integrate and link together more and more data points That’s complicated by the fact that nowadays, research, especially in life sciences, is not limited to one technology or even one discipline. The German Centre for Diabetes Research, where we work, is a multi-centre organisation that combines all the different data that originates from different studies, reports, surveys and research projects from different locations in in the country. We have masses of data from clinical trials and patient information, and our data covers various disciplines, from studies on molecular level to pathway analyses and animal models. To answer the interesting and suggestive biomedical questions about diabetes, we have to connect this data and look for new insights, patterns and correlations. That’s because we realise it is no longer enough to answer a biological or medical question from one direction, we need to integrate and link more and more data.
This is the next step, not just in biomedicine, but also in the healthcare sector, which is increasingly turning away
24 Scientific Computing World August/September 2018
from general blockbuster drugs and moving to individualised treatment or precision medicine. For this to progress, it is necessary to network significantly more and, above all, look at as many aspects of the problem as we can. This is why the DZD and other researchers think graph databases – the technology that powered the Paradise Papers investigation – could help in the prevention, discovery of new subtypes, early diagnosis and treatment of major illnesses. It’s important to know that we aren’t just using Excel or standard business relational (table) databases any more – we add a whole new layer with graph databases. The standard technology we use in each of our research
locations in Germany is a relational database, as well as spreadsheets and document files. But once we realised more and more of that data is connected, we started looking for a solution to bring our data closer in relation to each other, and create an overall context for our research. Relational databases have their merit. However, we needed something to bring these data silos together and uncover connections – to be able to jump from one data point to another is crucial for us. That’s why we turned to graph technology. To see why, be aware
diabetes is a metabolic disease, but it’s not sufficient for researchers to only look through metabolic data. They also have to take into account
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