The laboratory is at the forefront of integrating cutting-edge computational approaches with experimental immunology to unravel the complexities of the adaptive immune system. Our primary focus lies in advancing computational immunology, leveraging innovative machine learning and bioinformatics methods to explore the immune repertoire in health and disease.
A key area of interest in our lab is immune receptor profiling, where we aim to decode the diversity and specificity of B and T cell receptors. By combining high-throughput immune sequencing with state-of-the-art computational pipelines, we strive to map the immunogenic landscape in various contexts, including cancer, organ transplantation, autoimmunity, and infectious diseases.
Our team develops novel algorithms and tools tailored for the analysis of immune receptor repertoires, enabling precise characterization of clonotypes, identification of antigen-specific clones, and elucidation of the molecular underpinnings of immune responses. These tools also incorporate methods for MHC mismatch analysis to predict and mitigate immunogenicity in transplantation, as well as approaches inspired by artificial immune systems for machine learning applications.
Our research not only contributes to fundamental immunology but also addresses translational applications, such as biomarker discovery and personalized immunotherapy. By leveraging an interdisciplinary approach, the lab aims to push the boundaries of how we understand, model, and manipulate the immune system to improve human health.
| Github | Publication | |
|---|---|---|
| scRepertoire | link | link |
| escape | link | link |
| Trex | link | link |
| Ibex | link | preprint |
| immApex | link | in progress |
| immReferent | link | in progress |
| bHIVE | link | in progress |
