Knowledge Discovery for Human Genetic Diseases
The growth of biomedical data now exceeds human comprehension, necessitating computational methods to integrate data and make new discoveries. The Leiden Biosemantics group applies a multidisciplinary ‘enhanced science’ (e-Science) approach to optimally exploit the expertise of biologists, bioinformaticians, and computer scientists. In our vision, biomedical researchers will use more and more advanced computational methods as part of their normal routine.
We work towards this vision in two principle ways.
First, we build automated knowledge discovery methods that help us interpret very large data-sets, so as to increase our understanding of the biomolecular mechanisms that underlie human genetic diseases, and to expose actionable information that can shorten the path to new health interventions. For instance, we develop prioritization methods for genes, biomarkers, drug targets, and drug candidates.
Secondly, we are actively engaged in finding solutions for large-scale data interlinking, including the development of new publication models that incentivize data sharing and machine readability. We have created the Nanopublication standard: a new way to publish atomic biological relations in computer readable format along with metadata about the origin of the relations (http://nanopub.org). Nanopublications are citable, giving researchers who comply with the standard new opportunities for sharing their data and receiving credit for their work. We also launched the ‘FAIR’ data initiative, a step-by-step protocol for ensuring data is Findable, Accessible, Interoperable, and Reusable by both humans and computers.
The head of the Biosemantics Group, Barend Mons, is also co-leading the GO FAIR initiative, an initiative to kick start developments towards the Internet of FAIR data and services, which will also contribute to the implementation of components of the European Open Science Cloud.