ArticleBMC bioinformatics2021
SwarmTCR: a computational approach to predict the specificity of T cell receptors.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Structural T-Cell Receptor Analysis in the Age of Machine Learning.Immunological reviews · 2026Review
- Modeling TCR-Epitope Recognition Specificity: What We Should Learn to Succeed.Immunological reviews · 2026Review
- T-cell receptor insights: Determinants of Major Histocompatibility Complex class I versus class II recognition.Protein science : a publication of the Protein Society · 2025Article
- T cell receptor cross-reactivity prediction improved by a comprehensive mutational scan database.Cell systems · 2025Article
- TRain: T-cell receptor automated immunoinformatics.BMC bioinformatics · 2025Article
- T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity.Communications biology · 2025Article
- A fingerprint approach to pioneer structure-based T cell receptor repertoire analysis and specificity prediction.Frontiers in immunology · 2025Article
- Limits on inferring T cell specificity from partial information.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Predicting Antigen-Specificities of Orphan T Cell Receptors from Cancer Patients with TCRpcDist.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Predicting TCR sequences for unseen antigen epitopes using structural and sequence features.Briefings in bioinformatics · 2024Article
- Editorial: Quantification and prediction of T-cell cross-reactivity through experimental and computational methods.Frontiers in immunology · 2024Article
- Can we predict T cell specificity with digital biology and machine learning?Nature reviews. Immunology · 2023Review
- Quantitative approaches for decoding the specificity of the human T cell repertoire.Frontiers in immunology · 2023Review
- Stitchr: stitching coding TCR nucleotide sequences from V/J/CDR3 information.Nucleic acids research · 2022Article
- Next-generation sequencing: unraveling genetic mechanisms that shape cancer immunotherapy efficacy.The Journal of clinical investigation · 2022Review
- TCR-pMHC Binding Specificity Prediction From Structure Using Graph Neural Networks.IEEE transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundWith more T cell receptor sequence data becoming available, the need for bioinformatics approaches to predict T cell receptor specificity is even more pressing. Here we present SwarmTCR, a method that uses labeled sequence data to predict the specificity of T cell receptors using a nearest-neighbor approach. SwarmTCR works by optimizing the weights of the individual CDR regions to maximize classification performance.
resultsWe compared the performance of SwarmTCR against another nearest-neighbor method and showed that SwarmTCR performs well both with bulk sequencing data and with single cell data. In addition, we show that the weights returned by SwarmTCR are biologically interpretable.
conclusionsComputationally predicting the specificity of T cell receptors can be a powerful tool to shed light on the immune response against infectious diseases and cancers, autoimmunity, cancer immunotherapy, and immunopathology. SwarmTCR is distributed freely under the terms of the GPL-3 license. The source code and all sequencing data are available at GitHub ( https://github.com/thecodingdoc/SwarmTCR ).
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