Enabling efficient and safe application of CRISPR-Cas in primary human cells by deep learning-based information transfer from well-investigated cell types

Project summary

We hypothesize that the integration of various sources of genetic and epigenetic information is necessary for enabling the efficient and safe application of CRISPR-Cas in a therapeutic context. In this project, we aim at developing a deep learning-based approach for cell type-specific prediction of efficacy and specificity of CRISPR-Cas9 nucleases that incorporates similarity between datasets in a pre-training strategy. Various types of information will be integrated, and we will investigate different approaches for the similarity between datasets. To strengthen the robustness, we will train the models to be aware of adversarial examples. Experimental validation in various therapeutically applied human cell types will enable us to fine-tune the models in an iterative process and ensure clinical relevance.

Our methods

  • Data-driven modeling
  • Neural networks
  • Pre-training

Principal investigator 1

Doctoral researcher position 1

(supervised by PI Backofen)


Data collection and analysis, data imputation, setting up the similarity measurements and network architectures, and performing model pretraining, model training, and model evaluation in various experimental settings.


  • Master’s degree or equivalent in computer science, mathematics, machine learning, artificial intelligence, bioinformatics, or a closely related subject
  • Highly motivated in research regarding machine learning and its applications in biomedicine
  • Knowledge of machine learning, bioinformatics, and/or medical informatics is a plus, but not necessary
  • Good programming skills in Python

Principal investigator 2

Doctoral researcher position 2

(supervised by PI Cathomen)


Collection and analysis of CRISPR-Cas9 DNA binding and cleavage data as well as the experimental validation of algorithms.


  • Master’s degree or equivalent in biology, biochemistry, pharmacy, or a closely related field
  • Interest in data analysis and the fundamentals of machine learning tools

Administrative Manager

Marc Schumacher

Institute of Medical Biometry and Statistics,
Faculty of Medicine and Medical Center –
University of Freiburg