Enabling efficient and safe application of CRISPR-Cas in primary human cells by deep learning-based information transfer from well-investigated cell types (A05)

• Subject Area: Bioinformatics and Theoretical Biology ; General Genetics and Functional Genome Biology
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 499552394

We will develop a deep learning approach for cell type-specific prediction of efficacy and specificity of CRISPR-Cas 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 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.

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1597: Small Data
• Applicant Institution: Albert-Ludwigs-Universität Freiburg
• Project Heads: Professor Dr. Rolf Backofen; Professor Dr. Toni Cathomen