Prof. Dr. Michael Altenbuchinger is heading the Medical Data Science group at the Department of Medical Bioinformatics, University Medical Center Göttingen. Michael studied physics at the Technical University of Munich (TUM), where he received his diploma in 2009. After that, he joined the group for applied quantum field theories of Prof. W. Weise at TUM, where he did his PhD in 2014 on the chiral dynamics of heavy-light mesons.

After his PhD, Michael joined the Department of Biostatistics headed by Prof. Rainer Spang at the University of Regensburg as postdoctoral researcher, where he worked on statistical methods for high-throughput genomic data. In early 2019, he joined the computational biology group of Prof. John Quackenbush at the Harvard T.H. Chan School of Public Health. There he focused on machine learning methods for the reconstruction of genomic networks. In February 2020, he joined the University of Hohenheim to establish an independent research group on computational biology, where he focuses on machine learning in the context of medical and biological questions.

Chronic Kidney Disease Nephrologist’s App (CKDNapp) is designed as a clinical decision support software that assists the nephrologist in treating patients suffering from chronic kidney disease. CKDNapp uses the complex relationships between various patient parameters, such as the patient’s age and gender, as well as his/her clinical parameters. To estimate such relationships is a typical problem for machine learning algorithms. In his subproject, Michael will design customized algorithms for the development of CKDNapp. Moreover, he will develop methodology to extract metabolite concentrations from NMR metabolic fingerprints of patient’s blood. Those concentrations are a rich source to investigate the effects of kidney disease on the patient’s metabolism.