These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
22. Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning. Luu AM; Leistico JR; Miller T; Kim S; Song JS Genes (Basel); 2021 Apr; 12(4):. PubMed ID: 33920780 [TBL] [Abstract][Full Text] [Related]
23. TCR-Pred: A new web-application for prediction of epitope and MHC specificity for CDR3 TCR sequences using molecular fragment descriptors. Smirnov AS; Rudik AV; Filimonov DA; Lagunin AA Immunology; 2023 Aug; 169(4):447-453. PubMed ID: 36929656 [TBL] [Abstract][Full Text] [Related]
24. NetTCR-2.1: Lessons and guidance on how to develop models for TCR specificity predictions. Montemurro A; Jessen LE; Nielsen M Front Immunol; 2022; 13():1055151. PubMed ID: 36561755 [TBL] [Abstract][Full Text] [Related]
25. Attentive Variational Information Bottleneck for TCR-peptide interaction prediction. Grazioli F; Machart P; Mösch A; Li K; Castorina LV; Pfeifer N; Min MR Bioinformatics; 2023 Jan; 39(1):. PubMed ID: 36571499 [TBL] [Abstract][Full Text] [Related]
26. ClusTCR: a python interface for rapid clustering of large sets of CDR3 sequences with unknown antigen specificity. Valkiers S; Van Houcke M; Laukens K; Meysman P Bioinformatics; 2021 Dec; 37(24):4865-4867. PubMed ID: 34132766 [TBL] [Abstract][Full Text] [Related]
27. Predicting T cell receptor functionality against mutant epitopes. Drost F; Dorigatti E; Straub A; Hilgendorf P; Wagner KI; Heyer K; López Montes M; Bischl B; Busch DH; Schober K; Schubert B Cell Genom; 2024 Sep; 4(9):100634. PubMed ID: 39151427 [TBL] [Abstract][Full Text] [Related]
28. TPBTE: A model based on convolutional Transformer for predicting the binding of TCR to epitope. Wu J; Qi M; Zhang F; Zheng Y Mol Immunol; 2023 May; 157():30-41. PubMed ID: 36966551 [TBL] [Abstract][Full Text] [Related]
29. GPU-Accelerated Discovery of Pathogen-Derived Molecular Mimics of a T-Cell Insulin Epitope. Whalley T; Dolton G; Brown PE; Wall A; Wooldridge L; van den Berg H; Fuller A; Hopkins JR; Crowther MD; Attaf M; Knight RR; Cole DK; Peakman M; Sewell AK; Szomolay B Front Immunol; 2020; 11():296. PubMed ID: 32184781 [TBL] [Abstract][Full Text] [Related]
30. Predicting recognition between T cell receptors and epitopes with TCRGP. Jokinen E; Huuhtanen J; Mustjoki S; Heinonen M; Lähdesmäki H PLoS Comput Biol; 2021 Mar; 17(3):e1008814. PubMed ID: 33764977 [TBL] [Abstract][Full Text] [Related]
31. Natural high-avidity T-cell receptor efficiently mediates regression of cancer/testis antigen 83 positive common solid cancers. Li Q; Hu W; Liao B; Song C; Li L J Immunother Cancer; 2022 Jul; 10(7):. PubMed ID: 35798537 [TBL] [Abstract][Full Text] [Related]
32. Accurate TCR-pMHC interaction prediction using a BERT-based transfer learning method. Zhang J; Ma W; Yao H Brief Bioinform; 2023 Nov; 25(1):. PubMed ID: 38040492 [TBL] [Abstract][Full Text] [Related]
33. Performance comparison of TCR-pMHC prediction tools reveals a strong data dependency. Deng L; Ly C; Abdollahi S; Zhao Y; Prinz I; Bonn S Front Immunol; 2023; 14():1128326. PubMed ID: 37143667 [TBL] [Abstract][Full Text] [Related]
34. PiTE: TCR-epitope Binding Affinity Prediction Pipeline using Transformer-based Sequence Encoder. Zhang P; Bang S; Lee H Pac Symp Biocomput; 2023; 28():347-358. PubMed ID: 36540990 [TBL] [Abstract][Full Text] [Related]
35. TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes. Meynard-Piganeau B; Feinauer C; Weigt M; Walczak AM; Mora T Proc Natl Acad Sci U S A; 2024 Jun; 121(24):e2316401121. PubMed ID: 38838016 [TBL] [Abstract][Full Text] [Related]
36. Exploring the pre-immune landscape of antigen-specific T cells. Pogorelyy MV; Fedorova AD; McLaren JE; Ladell K; Bagaev DV; Eliseev AV; Mikelov AI; Koneva AE; Zvyagin IV; Price DA; Chudakov DM; Shugay M Genome Med; 2018 Aug; 10(1):68. PubMed ID: 30144804 [TBL] [Abstract][Full Text] [Related]
37. Quantifiable predictive features define epitope-specific T cell receptor repertoires. Dash P; Fiore-Gartland AJ; Hertz T; Wang GC; Sharma S; Souquette A; Crawford JC; Clemens EB; Nguyen THO; Kedzierska K; La Gruta NL; Bradley P; Thomas PG Nature; 2017 Jul; 547(7661):89-93. PubMed ID: 28636592 [TBL] [Abstract][Full Text] [Related]
38. Identification of Epitope-Specific T Cells in T-Cell Receptor Repertoires. Gielis S; Moris P; Bittremieux W; De Neuter N; Ogunjimi B; Laukens K; Meysman P Methods Mol Biol; 2020; 2120():183-195. PubMed ID: 32124320 [TBL] [Abstract][Full Text] [Related]
39. Training of epitope-TCR prediction models with healthy donor-derived cancer-specific T cells. Flumens D; Gielis S; Bartholomeus E; Campillo-Davo D; van der Heijden S; Versteven M; De Reu H; Smits E; Ogunjimi B; Laukens K; Meysman P; Lion E Methods Cell Biol; 2024; 183():143-160. PubMed ID: 38548410 [TBL] [Abstract][Full Text] [Related]
40. Antigen identification for HLA class I- and HLA class II-restricted T cell receptors using cytokine-capturing antigen-presenting cells. Lee MN; Meyerson M Sci Immunol; 2021 Jan; 6(55):. PubMed ID: 33483338 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]