Biomedical Genomics
We study how the genome encodes different layers of information, specifically its own stability, repair, and mutagenesis . Using a combination of computational biology, machine learning, deep learning models for sequence data, clinical and experimental data from collaborating labs, we try to understand the underlying mechanisms despite their complexity, and look for routes to bring this knowledge into clinical use.
Our research
The Biomedical Genomics group employs computational techniques, machine learning, and deep learning approaches to assess and model DNA damage and repair processes, and mutagenesis.
The group established a novel technique for genome-wide measurements of oxidative DNA damage and contributed to the understanding of genome editing precision and the genome wide distribution of mutations. Work is underway to understand these processes in the context of different cancer types and as a consequence to different treatment regimens tissue specifically.
For this we use computational biology with different types of functional genomics data (AP-Seq, HiC, ChIP-Seq,...) and cancer genomics data and different deep learning techniques, such as cascade models on multimodal data, LSTMs, and natural language processing.
