BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

146 related articles for article (PubMed ID: 32711842)

  • 1. MHCflurry 2.0: Improved Pan-Allele Prediction of MHC Class I-Presented Peptides by Incorporating Antigen Processing.
    O'Donnell TJ; Rubinsteyn A; Laserson U
    Cell Syst; 2020 Jul; 11(1):42-48.e7. PubMed ID: 32711842
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methods.
    Zhang H; Lundegaard C; Nielsen M
    Bioinformatics; 2009 Jan; 25(1):83-9. PubMed ID: 18996943
    [TBL] [Abstract][Full Text] [Related]  

  • 3. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data.
    Reynisson B; Alvarez B; Paul S; Peters B; Nielsen M
    Nucleic Acids Res; 2020 Jul; 48(W1):W449-W454. PubMed ID: 32406916
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Predicting peptide presentation by major histocompatibility complex class I: an improved machine learning approach to the immunopeptidome.
    Boehm KM; Bhinder B; Raja VJ; Dephoure N; Elemento O
    BMC Bioinformatics; 2019 Jan; 20(1):7. PubMed ID: 30611210
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Ranking-Based Convolutional Neural Network Models for Peptide-MHC Class I Binding Prediction.
    Chen Z; Min MR; Ning X
    Front Mol Biosci; 2021; 8():634836. PubMed ID: 34079815
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Improving MHC class I antigen-processing predictions using representation learning and cleavage site-specific kernels.
    Lawrence PJ; Ning X
    Cell Rep Methods; 2022 Sep; 2(9):100293. PubMed ID: 36160050
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Neurons preferentially respond to self-MHC class I allele products regardless of peptide presented.
    Escande-Beillard N; Washburn L; Zekzer D; Wu ZP; Eitan S; Ivkovic S; Lu Y; Dang H; Middleton B; Bilousova TV; Yoshimura Y; Evans CJ; Joyce S; Tian J; Kaufman DL
    J Immunol; 2010 Jan; 184(2):816-23. PubMed ID: 20018625
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.
    Atkins TK; Solanki A; Vasmatzis G; Cornette J; Riedel M
    Front Immunol; 2023; 14():1288105. PubMed ID: 38292493
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Game of Omes: ribosome profiling expands the MHC-I immunopeptidome.
    Holly J; Yewdell JW
    Curr Opin Immunol; 2023 Aug; 83():102342. PubMed ID: 37247567
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Improved prediction of MHC-peptide binding using protein language models.
    Hashemi N; Hao B; Ignatov M; Paschalidis IC; Vakili P; Vajda S; Kozakov D
    Front Bioinform; 2023; 3():1207380. PubMed ID: 37663788
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A Mechanistic Model for Predicting Cell Surface Presentation of Competing Peptides by MHC Class I Molecules.
    Boulanger DSM; Eccleston RC; Phillips A; Coveney PV; Elliott T; Dalchau N
    Front Immunol; 2018; 9():1538. PubMed ID: 30026743
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors.
    Kacen A; Javitt A; Kramer MP; Morgenstern D; Tsaban T; Shmueli MD; Teo GC; da Veiga Leprevost F; Barnea E; Yu F; Admon A; Eisenbach L; Samuels Y; Schueler-Furman O; Levin Y; Nesvizhskii AI; Merbl Y
    Nat Biotechnol; 2023 Feb; 41(2):239-251. PubMed ID: 36203013
    [TBL] [Abstract][Full Text] [Related]  

  • 14. SUS(d6)pending MHC class I peptide presentation for cancer immunoevasion.
    Dersh D; Yewdell JW
    Cell Res; 2024 Feb; 34(2):97-98. PubMed ID: 37833358
    [No Abstract]   [Full Text] [Related]  

  • 15. State of the art and challenges in sequence based T-cell epitope prediction.
    Lundegaard C; Hoof I; Lund O; Nielsen M
    Immunome Res; 2010 Nov; 6 Suppl 2(Suppl 2):S3. PubMed ID: 21067545
    [TBL] [Abstract][Full Text] [Related]  

  • 16. MHCflurry 2.0: Improved Pan-Allele Prediction of MHC Class I-Presented Peptides by Incorporating Antigen Processing.
    O'Donnell TJ; Rubinsteyn A; Laserson U
    Cell Syst; 2020 Oct; 11(4):418-419. PubMed ID: 33091335
    [No Abstract]   [Full Text] [Related]  

  • 17. B602L-Fc fusion protein enhances the immunogenicity of the B602L protein of the African swine fever virus.
    Yang Y; Xia Q; Zhou L; Zhang Y; Guan Z; Zhang J; Li Z; Liu K; Li B; Shao D; Qiu Y; Ma Z; Wei J
    Front Immunol; 2023; 14():1186299. PubMed ID: 37426672
    [TBL] [Abstract][Full Text] [Related]  

  • 18. NetCleave: An Open-Source Algorithm for Predicting C-Terminal Antigen Processing for MHC-I and MHC-II.
    Farriol-Duran R; Vallejo-Vallés M; Amengual-Rigo P; Floor M; Guallar V
    Methods Mol Biol; 2023; 2673():211-226. PubMed ID: 37258917
    [TBL] [Abstract][Full Text] [Related]  

  • 19. TSNAD and TSNAdb: The Useful Toolkit for Clinical Application of Tumor-Specific Neoantigens.
    Wu J; Zhou Z
    Methods Mol Biol; 2023; 2673():167-174. PubMed ID: 37258913
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8
    Gfeller D; Schmidt J; Croce G; Guillaume P; Bobisse S; Genolet R; Queiroz L; Cesbron J; Racle J; Harari A
    Cell Syst; 2023 Jan; 14(1):72-83.e5. PubMed ID: 36603583
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.