ReviewFrontiers in immunology2022
Machine Learning Approaches to TCR Repertoire Analysis.
Review in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- Synergistic immune interactions between T cells and natural killer cells in allogeneic haematopoietic stem cell transplantation for acute myeloid leukaemia: current status and future directions.Annals of medicine · 2026Review
- Medical software for precision diagnostics of infection with immunoprofiling and artificial intelligence.Journal of translational medicine · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Decoding adaptive immunity: advanced strategies in T and B cell repertoire analysis.Journal of translational medicine · 2026Review
- Immune signatures of common exposures through co-occurrence of T-cell receptors in tens of thousands of donors.Frontiers in immunology · 2026Article
- Identification of Sjögren's disease-associated T cell receptor motifs through deep sequencing.JCI insight · 2025Article
- Comparative assessment of physiochemical metrics for the clustering of adaptive immune receptor repertoires.Nucleic acids research · 2025Article
- Peripheral Blood TCR Clonotype Diversity as a Biomarker for Colorectal Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- Beyond single biomarkers: multi-omics strategies to predict immunotherapy outcomes in blood cancers.Clinical and experimental medicine · 2025Review
- Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes.Diabetologia · 2025Review
- An in-depth understanding of the role and mechanisms of T cells in immune organ aging and age-related diseases.Science China. Life sciences · 2025Review
- Integration of T cell repertoire, CyTOF, genotyping and symptomatology data reveals subphenotypic variability in COVID-19 patients.Computational and structural biotechnology journal · 2025Article
- A fingerprint approach to pioneer structure-based T cell receptor repertoire analysis and specificity prediction.Frontiers in immunology · 2025Article
- Optimizing clustering of CDR3 sequences using natural language processing, Word2Vec, and KMeans.Frontiers in bioinformatics · 2025Article
- Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool.BMC bioinformatics · 2024Article
- Hepatitis E virus: from innate sensing to adaptive immune responses.Nature reviews. Gastroenterology & hepatology · 2024Review
- Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence.Molecules (Basel, Switzerland) · 2024Review
- Predictability of antigen binding based on short motifs in the antibody CDRH3.Briefings in bioinformatics · 2024Article
- BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire.Briefings in bioinformatics · 2024Article
- Can we predict T cell specificity with digital biology and machine learning?Nature reviews. Immunology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects.
Indexed as
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.