Quantitative Biology Center

Dr. Gisela Gabernet

Team Leader Research & Development Data Science

Contact:

 +49-7071-29-76416

gisela.gabernetspam prevention@qbic.uni-tuebingen.de

www.qbic.uni-tuebingen.de

Biography:

  • 2015-2018: PhD student in the Computer-Assisted Drug Design group, ETH Zurich, Switzerland
  • 2014: Scientific staff at Helmholtz-Zentrum Dresden-Rossendorf, Germany
  • 2012-2014: M.Sc. in Molecular Bioengineering, Technische Universität Dresden, Germany
  • 2008-2012: B.Sc. in Biotechnology, Universitat Autonoma de Barcelona, Spain

Scientific contributions:

You can find a detailed publication list on my Google Scholar profile.

  • Grisoni, F.*, Neuhaus, C. S.*, Gabernet, G.*, Müller, A. T., Hiss, J. A., & Schneider, G. (2018). Designing anticancer peptides by constructive machine learning. ChemMedChem, 13(13), 1300–1302.
  • Armbrecht, L.*, Gabernet, G.*, Kurth, F., Hiss, J. A., Schneider, G., & Dittrich, P. S. (2017). Characterisation of anticancer peptides at the single-cell level. Lab Chip, 17, 2933–2940.
  • Müller, A. T., Gabernet, G., Hiss, J. A., & Schneider, G. (2017). modlAMP: Python for antimicrobial peptides. Bioinformatics.
  • Schneider, P., Müller, A. T., Gabernet, G., Button, A. L., Posselt, G., Wessler, S., Hiss, J. A., Schneider, G. (2017). Hybrid network model for deep learning of chemical data: application to antimicrobial peptides. Molecular Informatics, 36(1–2), 1600011.
  • Müller, A. T., Kaymaz, A. C., Gabernet, G., Posselt, G., Wessler, S., Hiss, J. A., & Schneider, G. (2016). Sparse neural network models of antimicrobial peptide-activity relationships. Molecular Informatics, 35(11–12), 606–614.
  • Gabernet, G., Müller, A. T., Hiss, J. A., & Schneider, G. (2016). Membranolytic anticancer peptides. Medicinal Chemistry Communications, 7(12), 2232–2245.