- Machine learning methods for deciphering the human immune repertoire language. Deciphering the immune information (immune status, antigen binding) encoded into antibody and T-cell repertoires is of paramount importance for the development of vaccines, diagnostics and therapeutics and requires machine learning approaches (artificial intelligence). We focus on developing deep learning approaches to learn how to read and write the immune repertoire language.
- Single-cell immune repertoire and immuno-mass spectrometry methods. Our research focuses on developing experimental platforms that link droplet-based high-throughput single-cell sequencing with the large-scale mass-spectrometry analysis of serum antibody repertoires in order to resolve the diversity and specificity of the effector serum antibody repertoire at single antibody resolution.
What are the current driving questions in immune repertoire research?
Cell Syst, 13 (9), 683-686
Access to ground truth at unconstrained size makes simulated data as indispensable as experimental data for bioinformatics methods development and benchmarking
Bioinformatics (in press)
Reference-based comparison of adaptive immune receptor repertoires
Cell Rep Methods, 2 (8), 100269