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 *

604 related articles for article (PubMed ID: 18463140)

  • 21. Integrating peptides' sequence and energy of contact residues information improves prediction of peptide and HLA-I binding with unknown alleles.
    Luo F; Gao Y; Zhu Y; Liu J
    BMC Bioinformatics; 2013; 14 Suppl 8(Suppl 8):S1. PubMed ID: 23815611
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Predicting MHC class I epitopes in large datasets.
    Roomp K; Antes I; Lengauer T
    BMC Bioinformatics; 2010 Feb; 11():90. PubMed ID: 20163709
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Prediction of MHC class I binding peptides using profile motifs.
    Reche PA; Glutting JP; Reinherz EL
    Hum Immunol; 2002 Sep; 63(9):701-9. PubMed ID: 12175724
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Structure-based identification of MHC binding peptides: Benchmarking of prediction accuracy.
    Kumar N; Mohanty D
    Mol Biosyst; 2010 Dec; 6(12):2508-20. PubMed ID: 20953500
    [TBL] [Abstract][Full Text] [Related]  

  • 25. MetaMHC: a meta approach to predict peptides binding to MHC molecules.
    Hu X; Zhou W; Udaka K; Mamitsuka H; Zhu S
    Nucleic Acids Res; 2010 Jul; 38(Web Server issue):W474-9. PubMed ID: 20483919
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Identifying MHC class I epitopes by predicting the TAP transport efficiency of epitope precursors.
    Peters B; Bulik S; Tampe R; Van Endert PM; Holzhütter HG
    J Immunol; 2003 Aug; 171(4):1741-9. PubMed ID: 12902473
    [TBL] [Abstract][Full Text] [Related]  

  • 27. SVMHC: a server for prediction of MHC-binding peptides.
    Dönnes P; Kohlbacher O
    Nucleic Acids Res; 2006 Jul; 34(Web Server issue):W194-7. PubMed ID: 16844990
    [TBL] [Abstract][Full Text] [Related]  

  • 28. NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets.
    Nielsen M; Andreatta M
    Genome Med; 2016 Mar; 8(1):33. PubMed ID: 27029192
    [TBL] [Abstract][Full Text] [Related]  

  • 29. SVRMHC prediction server for MHC-binding peptides.
    Wan J; Liu W; Xu Q; Ren Y; Flower DR; Li T
    BMC Bioinformatics; 2006 Oct; 7():463. PubMed ID: 17059589
    [TBL] [Abstract][Full Text] [Related]  

  • 30. A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-1*04:01, SLA-2*04:01, and SLA-3*04:01.
    Pedersen LE; Rasmussen M; Harndahl M; Nielsen M; Buus S; Jungersen G
    Immunogenetics; 2016 Feb; 68(2):157-65. PubMed ID: 26572135
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Application of machine learning techniques in predicting MHC binders.
    Lata S; Bhasin M; Raghava GP
    Methods Mol Biol; 2007; 409():201-15. PubMed ID: 18450002
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Relationship between peptide selectivities of human transporters associated with antigen processing and HLA class I molecules.
    Daniel S; Brusic V; Caillat-Zucman S; Petrovsky N; Harrison L; Riganelli D; Sinigaglia F; Gallazzi F; Hammer J; van Endert PM
    J Immunol; 1998 Jul; 161(2):617-24. PubMed ID: 9670935
    [TBL] [Abstract][Full Text] [Related]  

  • 33. ProPred1: prediction of promiscuous MHC Class-I binding sites.
    Singh H; Raghava GP
    Bioinformatics; 2003 May; 19(8):1009-14. PubMed ID: 12761064
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Prediction of epitopes using neural network based methods.
    Lundegaard C; Lund O; Nielsen M
    J Immunol Methods; 2011 Nov; 374(1-2):26-34. PubMed ID: 21047511
    [TBL] [Abstract][Full Text] [Related]  

  • 35. 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]  

  • 36. RBM-MHC: A Semi-Supervised Machine-Learning Method for Sample-Specific Prediction of Antigen Presentation by HLA-I Alleles.
    Bravi B; Tubiana J; Cocco S; Monasson R; Mora T; Walczak AM
    Cell Syst; 2021 Feb; 12(2):195-202.e9. PubMed ID: 33338400
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Sequence conservation analysis and in silico human leukocyte antigen-peptide binding predictions for the Mtb72F and M72 tuberculosis candidate vaccine antigens.
    Mortier MC; Jongert E; Mettens P; Ruelle JL
    BMC Immunol; 2015 Oct; 16():63. PubMed ID: 26493839
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Benchmarking predictions of MHC class I restricted T cell epitopes in a comprehensively studied model system.
    Paul S; Croft NP; Purcell AW; Tscharke DC; Sette A; Nielsen M; Peters B
    PLoS Comput Biol; 2020 May; 16(5):e1007757. PubMed ID: 32453790
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Improving the prediction of HLA class I-binding peptides using a supertype-based method.
    Wang S; Bai Z; Han J; Tian Y; Shang X; Wang L; Li J; Wu Y
    J Immunol Methods; 2014 Mar; 405():109-20. PubMed ID: 24508661
    [TBL] [Abstract][Full Text] [Related]  

  • 40. An integrative approach to CTL epitope prediction: a combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions.
    Larsen MV; Lundegaard C; Lamberth K; Buus S; Brunak S; Lund O; Nielsen M
    Eur J Immunol; 2005 Aug; 35(8):2295-303. PubMed ID: 15997466
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 31.