Biomedical research generates vast amounts of data. New experimental technologies like DNA sequencing, metabolomics and proteomics drive the fast growth of available information and lead to a better understanding of the molecular organization of life.
But with big data comes a big question: How do we transform unstructured data into actionable knowledge? In the case of biomedical research, the key problem is to integrate the large pile of highly heterogenous data and use it for personalized therapies and drug development. Graph databases are an ideal way to represent biomedical knowledge and offer the necessary flexibility to keep up with scientific progress. A well-designed data model and Cypher queries can deliver in seconds what previously took days of manual analysis.
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Martin Preusse studied biochemistry at TU München and is currently finishing his PhD in bioinformatics at the Institute for Computational Biology (Helmholtz Zentrum München, Germany). He focuses on integration of heterogeneous experimental data sets to understand cellular decision-making. Next to academic research, he is working on a startup that develops data solutions for biomedical research and biotech industry.