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.
Lê Quý K, Chernigovskaya M, Stensland M, Singh S, Leem J, Revale S, Yadin DA, Nice FL, Povall C, Minns DH, Galson JD, Nyman TA, Snapkow I, Greiff V(2024) Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling NPJ Syst Biol Appl, 10(1), 73 DOI 10.1038/s41540-024-00402-z, PubMed 38997321
Brown AJ, White J, Shaw L, Gross J, Slabodkin A, Kushner E, Greiff V, Matsuda J, Gapin L, Scott-Browne J, Kappler J, Marrack P(2024) MHC heterozygosity limits T cell receptor variability in CD4 T cells Sci Immunol, 9(97), eado5295 DOI 10.1126/sciimmunol.ado5295, PubMed 38996008
Vu MH, Robert PA, Akbar R, Swiatczak B, Sandve GK, Haug DTT, Greiff V(2024) Linguistics-based formalization of the antibody language as a basis for antibody language models Nat Comput Sci, 4(6), 412-422 DOI 10.1038/s43588-024-00642-3, PubMed 38877120