Genome Data Science Group
The Genome Data Science lab at the Faculty of Technology is headed by Prof. Dr. Alexander Schönhuth, who has an adjunct affiliation with the Center for Biotechnology (CeBiTec). Our research is centered on the development of computational methods and models that deal with machine learning / data science, data structures, algorithms and statistical models that serve the purposes to arrange, analyze and exploit the rapidly amassing genome data. Thereby, we cover a broad range of algorithms, software and protocols, from primary sequence analysis on the one end to sophisticated algorithms addressing involved questions in genetics, genomics and diseases on the end of high-impact applications.
Education provided by our lab focuses on combinatorial/statistical algorithms and models for the efficient analysis of genome sequencing data, the design of appropriate data structures for putting large amounts of genomes into mutual context (computational pan-genomics), and the design of machine learning (in particular deep learning) architectures and protocols that enable to exploit the rapidly accumulating genome data. While real life impact, for example in terms of truly promoting our understanding about diseases is very important, we put a clear emphasis on the creativeness and the pleasure when designing algorithms, data structures and network architectures that arises in our daily work.