Data arising from high-throughput technologies cannot be analyzed without sophisticated bioinformatics. Genomics, as a key technology for studying regulatory networks in the cell, is tightly coupled to modern data analysis techniques collectively referred to as machine learning. Bioinformatics students that work in the labs of computational experts, such as the co-PIs from Duke and Berlin, are continuously developing new approaches; biology students need to be familiarized with these tools. In the context of the joint Duke-Berlin interdisciplinary projects, students will be exposed to a broad spectrum of computational techniques and how they are applied to experimental data for studying gene regulation. Machine learning methods in general, and recent deep-learning approaches in particular, are the toolset that is put to use here. 

Involved research groups: 

• Prof. Raluca Gordan (Duke University)

• Prof. Alexander Hartemink (Duke University)

• Dr. Martin Kircher (Charité)

• Prof. Michael Love (University of North Carolina at Chapel Hill)

• Prof. Sayan Mukherjee (Duke University)

• Prof. Dr. Uwe Ohler (Humboldt-Universität zu Berlin)

• Prof. Dr. Martin Vingron (MPI for Molecular Genetics)