The digital transformation in the life sciences leads to more and more data, which have to be stored and analyzed appropriately to realize their huge potential in medical research and health care. An important requirement in medical applications is to protect the privacy of patient data.
In biomedical research and development, data integration independent of the application is not feasible due to the complexity of the data. Therefore, data are wasted in unused silos and a lot of connections in the data are not evaluated and therefore not accessible for further analysis. Suitable big data approaches can transform such data into valuable knowledge resources by integrating the data and make them accessible for further analysis. Furthermore, the data can be evaluated by machine learning algorithms to identify disease mechanisms or appropriate therapy options, for example.
In the following some projects in the area of big data in life sciences are listed:
- HUMIT – Human-centered support of incrementally-interactive data integration using the example of high-throughoutput processes in Life Sciences
- Big Data Alliance – The Fraunhofer Alliance “Big Data” supports medical professionals with data-driven approaches in the fields of diagnosis and therapy, patient care and research
- Optiscell – System platform for marker-free identification and manipulation of solitary cells in biomanufactoring engineering
- GoSmart – Generic open-end simulation environment for minimally invasive cancer treatment