Prof. Dr. Tim Beißbarth
Director of the Department of Medical Bioinformatics
|since 10.2018: Professor, Head of the Institute of Medical Bioinformatics, University Medical Center Göttingen|
|2008-2018: Professor for Biostatistics of the group Statistical Bioinformatics in the Department of Medical Statistics at the University of Göttingen|
|2005-2011: Groupt leader at the Molecular Genome Analysis Department at German Cancer Research Center - DKFZ|
|2002-2005 Postdoctoral Fellow at the Bioinformatics Department at Walter and Eliza Hall Institute of Medical Research, Melbourne -WEHI|
|2001-2002: Postdoctoral Fellow at: Computational Molecular Biology - Max-Planck-Institut Molecular Genetics, Berlin Functional Genome Analysis- German Cancer Research Center, Heidelberg|
|6/2001/2002: Teaching of Bioinformatics courses at the Akademie fuer Weiterbildung Education|
|1998-2001: PhD thesis at the German Cancer Research Center- DKFZ Departments: Theoretical Bioinformatics and Molecular Biology of the Cell I|
|1996-1998: Diplom (Masters) thesis in the Departement of Cell Genetics about MHC Class I - TAP interaction|
|1996: Assistant in Biochemical Practical Courses at the Institute for Biochemistry|
|1995-1996: Worked at the Regional Computing Center - Object Oriented Programming Courses|
|1992-1997: Study of Biology and Computer Science at the University of Cologne|
|1989-1990: West Valley Highschool - Cottonwood, CA, USA|
|1983-1992: Heinrich-Heine-Gymnasium - Cologne, Germany|
|Sept. 8., 1972: Born in Cologne, Germany|
- Statistical Methods in Bioinformatics
- Modeling biological networks
- Integrative data analysis methods
- Machine learning
MyPathSem In the MyPathSem project, we aim in collaboration with other institutes of the UMG to come up with with a computational platform, or toolbox, which can be used by clinicians for making optimal use of high-throughput data for diagnostic or therapeutic purposes. For more information, please see the official project site.
MTB-Report: The aim of this project is to develop a system automatically matching genomic data to suitable treatment options, which will help clinicians to interpret genomic data in a fast and higly quantative manner. More.
MATCH: In Cooperation with the UMC Hamburg-Eppendorf or aim is the identification of a biomarker signature which associates with beneficial outcome on specific lipid therapy, and ideally is not dynamic under ongoing therapy. More.
PerMiCCion: Will start in March 2022. More soon.
Chereda H, Bleckmann A, Menck K, Perera-Bel J, Stegmaier P, Auer F, Kramer F, Leha A, Beißbarth T.:
Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer.
Genome Med. 2021 Mar 11;13(1):42.
Perera-Bel J., Hutter B., Heining C., Bleckmann A., Fröhlich M., Fröhlich S., Glimm H., Brors B., Beißbarth T.:
From somatic variants towards precision oncology: Evidence-driven reporting of treatment options in molecular tumor boards.
Genome Med. 10(1):18
Wolff A., Perera-bel J., Schildhaus HU, Homayounfar K., Schatlo B., Belckmann A., Beißbarth T.:
Using RNA-Seq Data for the Detection of a Panel of Clinically Relevant Mutations.
Stud. Health Technol. Inform. 253:217-221
Chereda H., Bleckmann A., Kramer F., Leha A., Beisbarth T.:
Utilizing Molecular Network Information via Graph Convolutional Neural Networks to Predict Metastatic Event in Breast Cancer.
Stud. health Technol. Inform. 2019 Sep 3;267:181-186
Sitte M., Menck K., Wachter A., Reinz E., Korf U., Wiemann S., Bleckmann A., Beissbarth T.:
Reconstruction of Different Modes of WNT Dependant Protein Networks from Time Series Protein Quantification.
Stud. health Technol. Inform. 2019 Sep 3;267:175-180
Wachter A., Beißbarth T.:
Decoding cellular dynamics in epidermal growth factor signaling using a new pathway-driven integration approach for proteomics and transcriptomics data.
Frontiers in genetics, 2015,6:351
Fröhlich H., Tresch A., Beißbarth T.:
Nested Effects Models for Learning Signaling Networks from Perturbation Data
Biometrical Journal, 2009, 51(2):304-23
Beißbarth T., Speed T.P.:
GOstat: Find statistically overrepresented Gene Ontologieswithin a group of genes.
Bioinformatics; 6.2004; 20(9): 1464-1465
Since 2020 DFG peer for subject Medical Informatics and Bioinformatics
Since 2019: In founding board of director of the Göttingen Campus Institute for Data Science (CIDAS)
Since 2017: Speaker of the field Medical Bioinformatics&Systemsbiology of the society GMDS
Since 2016: Member of the advisory board of the Fachgruppe Bioinformatik (FABI)
Since 2015: Associate Editor of GMS Medizinische Informatik, Biometrie und Epidemologie
Since 2014: In executive committee "Curricula for Medical Informatics" of the GMDS society
Since 2013 Member of the faculty board of the University Medical Center Göttingen
2009-2013: Leader of the joint workgroup "Statistical Method in Bioinformatics" of the scientific associations Internationla Biometrical Society (IBS) and German Society for medical Informatics, Biometry and Epidemiology (GMDS)
Since 2013: Co-leader of workgroup "Biomedical Informatics" of the society GMDS
Since 2012: Associate Editor of Statistical Applications in Molecular Biology (SAGMB).
Since 2011: Build up a Core-Facility "Medical Statistics&Bioinformatics" within the department of Medical Statistics for the University Medical Center Göttingen
Since 2009: Associate Editor of BMC Cancer
Since 2008: Associate Editor of BMC Bioinformatics