BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

231 related articles for article (PubMed ID: 18031584)

  • 1. Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms.
    Rajapakse M; Schmidt B; Feng L; Brusic V
    BMC Bioinformatics; 2007 Nov; 8():459. PubMed ID: 18031584
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan.
    Nielsen M; Lundegaard C; Blicher T; Peters B; Sette A; Justesen S; Buus S; Lund O
    PLoS Comput Biol; 2008 Jul; 4(7):e1000107. PubMed ID: 18604266
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach.
    Nielsen M; Lundegaard C; Worning P; Hvid CS; Lamberth K; Buus S; Brunak S; Lund O
    Bioinformatics; 2004 Jun; 20(9):1388-97. PubMed ID: 14962912
    [TBL] [Abstract][Full Text] [Related]  

  • 6. In silico design of MHC class I high binding affinity peptides through motifs activation map.
    Xiao Z; Zhang Y; Yu R; Chen Y; Jiang X; Wang Z; Li S
    BMC Bioinformatics; 2018 Dec; 19(Suppl 19):516. PubMed ID: 30598069
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models.
    Liu W; Meng X; Xu Q; Flower DR; Li T
    BMC Bioinformatics; 2006 Mar; 7():182. PubMed ID: 16579851
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Learning a peptide-protein binding affinity predictor with kernel ridge regression.
    Giguère S; Marchand M; Laviolette F; Drouin A; Corbeil J
    BMC Bioinformatics; 2013 Mar; 14():82. PubMed ID: 23497081
    [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. 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]  

  • 11. Structural properties of MHC class II ligands, implications for the prediction of MHC class II epitopes.
    Jørgensen KW; Buus S; Nielsen M
    PLoS One; 2010 Dec; 5(12):e15877. PubMed ID: 21209859
    [TBL] [Abstract][Full Text] [Related]  

  • 12. NetMHCpan, a method for MHC class I binding prediction beyond humans.
    Hoof I; Peters B; Sidney J; Pedersen LE; Sette A; Lund O; Buus S; Nielsen M
    Immunogenetics; 2009 Jan; 61(1):1-13. PubMed ID: 19002680
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Efficient peptide-MHC-I binding prediction for alleles with few known binders.
    Jacob L; Vert JP
    Bioinformatics; 2008 Feb; 24(3):358-66. PubMed ID: 18083718
    [TBL] [Abstract][Full Text] [Related]  

  • 14. PromPDD, a web-based tool for the prediction, deciphering and design of promiscuous peptides that bind to HLA class I molecules.
    Zhang S; Chen J; Hong P; Li J; Tian Y; Wu Y; Wang S
    J Immunol Methods; 2020 Jan; 476():112685. PubMed ID: 31678214
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Prediction of human major histocompatibility complex class II binding peptides by continuous kernel discrimination method.
    He J; Yang G; Rao H; Li Z; Ding X; Chen Y
    Artif Intell Med; 2012 Jun; 55(2):107-15. PubMed ID: 22134095
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Learning MHC I--peptide binding.
    Jojic N; Reyes-Gomez M; Heckerman D; Kadie C; Schueler-Furman O
    Bioinformatics; 2006 Jul; 22(14):e227-35. PubMed ID: 16873476
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Class I MHC-peptide interaction: structural and functional aspects.
    Ruppert J; Kubo RT; Sidney J; Grey HM; Sette A
    Behring Inst Mitt; 1994 Jul; (94):48-60. PubMed ID: 7998914
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Towards the in silico identification of class II restricted T-cell epitopes: a partial least squares iterative self-consistent algorithm for affinity prediction.
    Doytchinova IA; Flower DR
    Bioinformatics; 2003 Nov; 19(17):2263-70. PubMed ID: 14630655
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 12.