ArticleNature machine intelligence2021
The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires.
Article in Nature machine intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
57 citing papers in PubMed.
- Single-cell immune repertoire atlas maps coordinated circulating adaptive immune states in inflammatory bowel disease.bioRxiv : the preprint server for biology · 2026Article
- Naïve adaptive immune receptor repertoires in celiac disease assessed by machine learning; impact of the HLA-DQ2.5 allotype on the TCR repertoire.Immunogenetics · 2026Article
- What Do Generative Models Learn About Adaptive Immune Receptor Repertoires? A Benchmark Study.bioRxiv : the preprint server for biology · 2026Article
- PEPE: scalable extraction of multi-modal protein language model representations.Bioinformatics (Oxford, England) · 2026Article
- Revised Adaptive Immune Receptor Data in the Immune Epitope Database.bioRxiv : the preprint server for biology · 2026Article
- Chronic Stress, Immune Suppression, and Cancer Occurrence: Unveiling the Connection Using Survey Data and Predictive Models.Medical sciences (Basel, Switzerland) · 2026Article
- AI-Driven BCR Modeling for Precision Immunology.International journal of molecular sciences · 2026Review
- Medical software for precision diagnostics of infection with immunoprofiling and artificial intelligence.Journal of translational medicine · 2026Review
- Genomic analysis of T Cell receptors reveals lynch syndrome specific immune signatures.Nature communications · 2026Article
- AMULETY: A Python package to embed adaptive immune receptor sequences.Immunoinformatics (Amsterdam, Netherlands) · 2026Article
- Decoding adaptive immunity: advanced strategies in T and B cell repertoire analysis.Journal of translational medicine · 2026Review
- Identification of a type 1 diabetes-associated T cell receptor repertoire signature from the human peripheral blood.Science advances · 2026Article
- A multi-branch ensemble learning framework for detection of non-small cell lung cancer via T-cell receptor sequencing.BMC cancer · 2026Article
- Explore antibody repertoire in the era of AI.Acta biochimica et biophysica Sinica · 2025Article
- Machine learning in AIRR diagnostics: Advances and applications.Immunoinformatics (Amsterdam, Netherlands) · 2025Article
- Comparative assessment of physiochemical metrics for the clustering of adaptive immune receptor repertoires.Nucleic acids research · 2025Article
- Beyond single biomarkers: multi-omics strategies to predict immunotherapy outcomes in blood cancers.Clinical and experimental medicine · 2025Review
- Role of artificial intelligence in advancing immunology.Immunologic research · 2025Review
- Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes.Diabetologia · 2025Review
- The compositional behavior of the human T cell receptor repertoire in ovarian cancer compared to healthy donors.Scientific data · 2025Article
Corrections and comments
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Authors and funding
42 authors.
Funding
Abstract
Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML (immuneml.uio.no) 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. 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. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel deep learning method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.