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IRTG2403 - Regulatory Genome

Computational biology approaches for understanding gene regulation from heterogeneous data

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. 

 

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