Prof. Dr. Tim Beißbarth
Director of the Department of Medical Bioinformatics
Telefon: 0551 3914912
Telefax: 0551 14914
E-Mail: tim.beißbarth@bioinf.med.uni-goettingen.de
Ort: Goldschmidtstr. 1, 2.OG, Room: 2.107
Curriculum Vitae
Academic Carrer
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
Research focus
Statistical Methods in Bioinformatics
Modeling biological networks
Integrative data analysis methods
Machine learning
Current projects
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.
PerMiCCion: Will start in March 2022. More soon.
Selected Publications
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.
doi: 10.1186/s13073-021-00845-7
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
doi: 10.1186/s13073-018-0529-2
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
Professional activities
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