Beißbarth, Tim, Prof. Dr.

Head of Department Medical Bioinformatics

  • 2001 Dr. rer. nat, University Heidelberg
  • 2001 – 2002 Postdoctoral fellow, Department Computational Molecular Biology, Max-Planck-Institute for molecular Genetics, Berlin
  • 2002 – 2005 Postdoctoral fellow, Department Bioinformatics, WEHI, Melbourne, Australia
  • 2005 – 2008 Group Leader, Bioinformatics & Modeling, Department Molecular Genome Analysis, DKFZ, Heidelberg
  • 2008 – 2018 Professor, Statistical Bioinformatics, Department Medical Statistics, University Medical Center, Göttingen
  • since 2018 Professor, Head of Department Medical Bioinformatics, University Medical Center, Göttingen

Major Research Interests

The Department of Medical Bioinformatics is developing methods in Statistical Bioinformatics as well as Systems Medicine for biomedical research. We are collaborating in biomedical research projects and working in interdisciplinary consortia on the analysis of large heterogeneous high-throughput data-sets. There we apply mainly machine learning approaches as well as analysis and reconstruction methods for biological networks. The focus of the department is the development of methods and tools for the integrative analysis of large biomedical data-sets. These methods are implemented mostly in the statistical computing environment of R.

Homepage Department/Research Group

Selected Recent Publications

  • Chereda H, Bleckmann A, Kramer F, Leha A, Beißbarth T (2019) Utilizing Molecular Network Information via Graph Convolutional Neural Networks to Predict Metastatic Event in Breast Cancer. Stud Health Technol Inform 267:181-186
  • Sitte M, Menck K, Wachter A, Reinz E, Korf U, Wiemann S, Bleckmann A, Beißbarth T (2019) Reconstruction of Different Modes of WNT Dependent Protein Networks from Time Series Protein Quantification. Stud Health Technol Inform 267:175-180
  • Perera-Bel J, Hutter B, Heining C, Bleckmann A, Fröhlich M, Fröhling S, Glimm H, Brors B, Beißbarth T (2018) 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, Bleckmann A, Beißbarth T (2018) Using RNA-Seq Data for the Detection of a Panel of Clinically Relevant Mutations. Stud Health Technol Inform 253:217-221
  • Wolff A, Bayerlová M, Gaedcke J, Kube D, Beißbarth T (2018) A comparative study of RNA-Seq and microarray data analysis on the two examples of rectal-cancer patients and Burkitt Lymphoma cells. PLoS One 13(5):e0197162
  • Kramer F, Beißbarth T (2017) Working with Ontologies. Methods Mol Biol 1525:123-135
  • Wachter A, Beißbarth T (2016) Decoding Cellular Dynamics in Epidermal Growth Factor Signaling Using a New Pathway-Based Integration Approach for Proteomics and Transcriptomics Data. Front Genet 6:351
  • Becker K, Stauber M, Schwarz F, Beißbarth T (2015) Automated 3D-2D registration of X-ray microcomputed tomography with histological sections for dental implants in bone using chamfer matching and simulated annealing. Comput Med Imaging Graph 44:62-8