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.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

166 related articles for article (PubMed ID: 34096999)

  • 1. BERTMHC: improved MHC-peptide class II interaction prediction with transformer and multiple instance learning.
    Cheng J; Bendjama K; Rittner K; Malone B
    Bioinformatics; 2021 Nov; 37(22):4172-4179. PubMed ID: 34096999
    [TBL] [Abstract][Full Text] [Related]  

  • 2. DeepMHCII: a novel binding core-aware deep interaction model for accurate MHC-II peptide binding affinity prediction.
    You R; Qu W; Mamitsuka H; Zhu S
    Bioinformatics; 2022 Jun; 38(Suppl 1):i220-i228. PubMed ID: 35758790
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Investigating the human and nonobese diabetic mouse MHC class II immunopeptidome using protein language modeling.
    Hartout P; Počuča B; Méndez-García C; Schleberger C
    Bioinformatics; 2023 Aug; 39(8):. PubMed ID: 37527005
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method.
    Nielsen M; Lundegaard C; Lund O
    BMC Bioinformatics; 2007 Jul; 8():238. PubMed ID: 17608956
    [TBL] [Abstract][Full Text] [Related]  

  • 5. MultiRTA: a simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes.
    Bordner AJ; Mittelmann HD
    BMC Bioinformatics; 2010 Sep; 11():482. PubMed ID: 20868497
    [TBL] [Abstract][Full Text] [Related]  

  • 6. MHC2MIL: a novel multiple instance learning based method for MHC-II peptide binding prediction by considering peptide flanking region and residue positions.
    Xu Y; Luo C; Qian M; Huang X; Zhu S
    BMC Genomics; 2014; 15 Suppl 9(Suppl 9):S9. PubMed ID: 25521198
    [TBL] [Abstract][Full Text] [Related]  

  • 7. MHCAttnNet: predicting MHC-peptide bindings for MHC alleles classes I and II using an attention-based deep neural model.
    Venkatesh G; Grover A; Srinivasaraghavan G; Rao S
    Bioinformatics; 2020 Jul; 36(Suppl_1):i399-i406. PubMed ID: 32657386
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Towards universal structure-based prediction of class II MHC epitopes for diverse allotypes.
    Bordner AJ
    PLoS One; 2010 Dec; 5(12):e14383. PubMed ID: 21187956
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Predicting Class II MHC-Peptide binding: a kernel based approach using similarity scores.
    Salomon J; Flower DR
    BMC Bioinformatics; 2006 Nov; 7():501. PubMed ID: 17105666
    [TBL] [Abstract][Full Text] [Related]  

  • 10. RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding.
    Wang X; Wu T; Jiang Y; Chen T; Pan D; Jin Z; Xie J; Quan L; Lyu Q
    Bioinformatics; 2024 Jan; 40(1):. PubMed ID: 38175759
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes.
    Zhao W; Sher X
    PLoS Comput Biol; 2018 Nov; 14(11):e1006457. PubMed ID: 30408041
    [TBL] [Abstract][Full Text] [Related]  

  • 12. DeepLigand: accurate prediction of MHC class I ligands using peptide embedding.
    Zeng H; Gifford DK
    Bioinformatics; 2019 Jul; 35(14):i278-i283. PubMed ID: 31510651
    [TBL] [Abstract][Full Text] [Related]  

  • 13. STMHCpan, an accurate Star-Transformer-based extensible framework for predicting MHC I allele binding peptides.
    Ye Z; Li S; Mi X; Shao B; Dai Z; Ding B; Feng S; Sun B; Shen Y; Xiao Z
    Brief Bioinform; 2023 May; 24(3):. PubMed ID: 37122066
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity: in silico bioinformatic step-by-step guide using quantitative structure-activity relationships.
    Hattotuwagama CK; Doytchinova IA; Flower DR
    Methods Mol Biol; 2007; 409():227-45. PubMed ID: 18450004
    [TBL] [Abstract][Full Text] [Related]  

  • 15. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction.
    Nielsen M; Lund O
    BMC Bioinformatics; 2009 Sep; 10():296. PubMed ID: 19765293
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Improved peptide-MHC class II interaction prediction through integration of eluted ligand and peptide affinity data.
    Garde C; Ramarathinam SH; Jappe EC; Nielsen M; Kringelum JV; Trolle T; Purcell AW
    Immunogenetics; 2019 Jul; 71(7):445-454. PubMed ID: 31183519
    [TBL] [Abstract][Full Text] [Related]  

  • 17. MHCSeqNet2-improved peptide-class I MHC binding prediction for alleles with low data.
    Wongklaew P; Sriswasdi S; Chuangsuwanich E
    Bioinformatics; 2024 Jan; 40(1):. PubMed ID: 38152987
    [TBL] [Abstract][Full Text] [Related]  

  • 18. PepDist: a new framework for protein-peptide binding prediction based on learning peptide distance functions.
    Hertz T; Yanover C
    BMC Bioinformatics; 2006 Mar; 7 Suppl 1(Suppl 1):S3. PubMed ID: 16723006
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Determination of a Predictive Cleavage Motif for Eluted Major Histocompatibility Complex Class II Ligands.
    Paul S; Karosiene E; Dhanda SK; Jurtz V; Edwards L; Nielsen M; Sette A; Peters B
    Front Immunol; 2018; 9():1795. PubMed ID: 30127785
    [TBL] [Abstract][Full Text] [Related]  

  • 20. ACME: pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks.
    Hu Y; Wang Z; Hu H; Wan F; Chen L; Xiong Y; Wang X; Zhao D; Huang W; Zeng J
    Bioinformatics; 2019 Dec; 35(23):4946-4954. PubMed ID: 31120490
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.