Dr. Jan Baumbach

Technical University Munich, Germany

Dr. Jan Baumbach studied Applied Computer Science in the Natural Sciences at Bielefeld University in Germany. His research career started at Rothamsted Research in Harpenden (UK) where he worked on computational methods for the integration of molecular biology data. He returned to the Center for Biotechnology in Bielefeld for his PhD studies on the reconstruction of bacterial transcriptional regulatory networks. He developed CoryneRegNet, the reference database and analysis platform for corynebacterial gene regulations. Afterwards, at the University of California at Berkeley, he worked in the Algorithms group of Richard Karp on protein homology detection. In Berkeley, he also developed Transitivity Clustering, a novel clustering framework for large-scale biomedical data sets. From March 2010, Jan was head of the Computational Systems Biology group at the Max Planck Institute for Informatics in Saarbrücken, Germany.

In October 2012, he moved to the University of Southern Denmark as head of the Computational BioMedicine group. His research concentrated on the combined analysis of biological networks together with OMICS data, the modeling of genetic expression pathways as well as biomarker discovery and computational methods for precision medicine. He was study program coordinator of Computational BioMedicine program from 2015 to 2017. In January 2018 he moved to the Technical University of Munich as chair of the Experimental Bioinformatics. In Munich, Jan develops computational methods for Systems Medicine and novel federated AI approaches ensuring privacy by design.


DAY 2: September 12, 2019 | Featured Speaker | 1:50 PM - 2:20 PM

What I Learned About Arnold Schwarzenegger While Studying Breast Cancer?

Jan Baumbach, Ph.D, Technical University Munich, Germany.

One major obstacle in current medicine and drug development is inherent in the way we define and approach diseases. We discuss the diagnostic and prognostic value of (multi-)omics panels. We have a closer look at breast cancer survival and treatment outcome, as case example, using gene expression panels - and we will discuss the current "best practice" in the light of critical statistical considerations. In addition, we introduce computational approaches for network-based medicine. We discuss novel developments in graph-based machine learning using examples ranging from Huntington's disease mechanisms via Alzheimer's drug target discovery back to where we started, i.e. breast cancer treatment optimization - but now from a systems medicine point of view. We conclude that multi-scale network medicine and modern artificial intelligence open new avenues to shape future medicine.


DAY 3: September 13, 2019 | Session CO-CHAIR | 9:20 AM – 10:30 AM

Jan Baumbach, Ph.D, Technical University Munich, Germany.