Altenbuchinger, Michael, Prof. Dr.

Professor for Bioinformatics

2010 – 2014: Doctoral Researcher, Physics Department, Technical University of Munich, Munich, Germany
2014 – 2019: Postdoctoral Fellow, University of Regensburg, Statistical Bioinformatics, Regensburg, Germany
2019 – 2020: Postdoctoral Fellow, Harvard T.H. Chan School of Public Health, Department of Biostatistics, Boston, MA, USA
2020 – 2021: Junior Research Group Leader “Computational Biology”, University of Hohenheim, Stuttgart, Germany
2021 – now: W2 professor for Bioinformatics, Department of Medical Bioinformatics, University Medical Center Goettingen, Göttingen, Germany

Major Research Interests

The group for medical data science develops computational approaches for the analysis of biomedical data. This comprises methods from high-dimensional statistics and from machine learning. We are highly interested in developing methods for the integration of multiple different data sources, such as multiple -omics layers, and in medical applications, such as optimizing treatment of cancer and chronic kidney disease patients.

Homepage Department/Research Group

https://bioinformatics.umg.eu/research/workgroups/medical-data-science/

Selected Recent Publications

Zacharias, H. U., Altenbuchinger, M., Schultheiss, U. T., Raffler, J., Kotsis, F., Ghasemi, S., ... & Oefner, P. J. (2021). A Predictive Model for Progression of CKD to Kidney Failure Based on Routine Laboratory Tests. American Journal of Kidney Diseases

Jachimowicz, R. D., Klapper, W., Glehr, G., Müller, H., Haverkamp, H., Thorns, C., ... & Rosenwald, A. (2021). Gene expression-based outcome prediction in advanced stage classical Hodgkin lymphoma treated with BEACOPP. Leukemia, 1-5

Staiger, A. M., Altenbuchinger, M., Ziepert, M., Kohler, C., Horn, H., Huttner, M., ... & Spang, R. (2020). A novel lymphoma-associated macrophage interaction signature (LAMIS) provides robust risk prognostication in diffuse large B-cell lymphoma clinical trial cohorts of the DSHNHL. Leukemia, 34(2), 543-552

Altenbuchinger, M., Zacharias, H. U., Solbrig, S., Schäfer, A., Büyüközkan, M., Schultheiß, U. T., ... & Gronwald, W. (2019). A multi-source data integration approach reveals novel associations between metabolites and renal outcomes in the German Chronic Kidney Disease study. Scientific reports, 9(1), 1-13

Görtler, F., Schoen, M., Simeth, J., Solbrig, S., Wettig, T., Oefner, P. J., ... & Altenbuchinger, M. (2020). Loss-function learning for digital tissue deconvolution. Journal of Computational Biology, 27(3), 342-355