H., Greiff, V., Karatt-Vellatt, A., Muyldermans, S. Predicting antigen specificity of single T cells based on TCR CDR3 regions. Machine learning for biologics: opportunities for protein engineering, developability, and formulation. A review of deep learning methods for antibodies. Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification. ![]() Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning. Convergent selection in antibody repertoires is revealed by deep learning. Prediction of specific TCR-peptide binding from large dictionaries of TCR-peptide pairs. Springer, I., Besser, H., Tickotsky-Moskovitz, N., Dvorkin, S. Identifying specificity groups in the T cell receptor repertoire. Quantifiable predictive features define epitope-specific T cell receptor repertoires. A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding. Mining adaptive immune receptor repertoires for biological and clinical information using machine learning. The future of blood testing is the immunome. T cell receptor β repertoires as novel diagnostic markers for systemic lupus erythematosus and rheumatoid arthritis. De novo prediction of cancer-associated T cell receptors for noninvasive cancer detection. Biophysicochemical motifs in T cell receptor sequences distinguish repertoires from tumor-infiltrating lymphocytes and adjacent healthy tissue. Machine learning analysis of naïve B-cell receptor repertoires stratifies celiac disease patients and controls. Sex bias in MHC I-associated shaping of the adaptive immune system. Age-related decrease in TCR repertoire diversity measured with deep and normalized sequence profiling. Genetic and environmental determinants of human TCR repertoire diversity. Krishna, C., Chowell, D., Gönen, M., Elhanati, Y. ![]() Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire. Human T cell receptor occurrence patterns encode immune history, genetic background, and receptor specificity. Immune literacy: reading, writing, and editing adaptive immunity. ![]() Practical guidelines for B-cell receptor repertoire sequencing analysis. The promise and challenge of high-throughput sequencing of the antibody repertoire. Augmenting adaptive immunity: progress and challenges in the quantitative engineering and analysis of adaptive immune receptor repertoires. We demonstrate the broad applicability of immuneML by (1) reproducing a large-scale study on immune state prediction, (2) developing, integrating and applying a novel deep learning method for antigen specificity prediction and (3) showcasing streamlined interpretability-focused benchmarking of AIRR ML.īrown, A. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. immuneML ( ) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. So far, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency and interoperability. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. Nature Machine Intelligence volume 3, pages 936–944 ( 2021) Cite this articleĪdaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires
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