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 *

100 related articles for article (PubMed ID: 18450015)

  • 1. Nonlinear predictive modeling of MHC class II-peptide binding using Bayesian neural networks.
    Winkler DA; Burden FR
    Methods Mol Biol; 2007; 409():365-77. PubMed ID: 18450015
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Predictive Bayesian neural network models of MHC class II peptide binding.
    Burden FR; Winkler DA
    J Mol Graph Model; 2005 Jun; 23(6):481-9. PubMed ID: 15878832
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Static energy analysis of MHC class I and class II peptide-binding affinity.
    Davies MN; Flower DR
    Methods Mol Biol; 2007; 409():309-20. PubMed ID: 18450011
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. A Bayesian regression approach to the prediction of MHC-II binding affinity.
    Zhang W; Liu J; Niu YQ; Wang L; Hu X
    Comput Methods Programs Biomed; 2008 Oct; 92(1):1-7. PubMed ID: 18562039
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Artificial intelligence methods for predicting T-cell epitopes.
    Zhao Y; Sung MH; Simon R
    Methods Mol Biol; 2007; 409():217-25. PubMed ID: 18450003
    [TBL] [Abstract][Full Text] [Related]  

  • 8. In silico prediction of peptide-MHC binding affinity using SVRMHC.
    Liu W; Wan J; Meng X; Flower DR; Li T
    Methods Mol Biol; 2007; 409():283-91. PubMed ID: 18450008
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Toward prediction of class II mouse major histocompatibility complex peptide binding affinity: in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique.
    Hattotuwagama CK; Toseland CP; Guan P; Taylor DJ; Hemsley SL; Doytchinova IA; Flower DR
    J Chem Inf Model; 2006; 46(3):1491-502. PubMed ID: 16711768
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A practical guide to structure-based prediction of MHC-binding peptides.
    Ranganathan S; Tong JC
    Methods Mol Biol; 2007; 409():301-8. PubMed ID: 18450010
    [TBL] [Abstract][Full Text] [Related]  

  • 11. QSAR method for prediction of protein-peptide binding affinity: application to MHC class I molecule HLA-A*0201.
    Zhao C; Zhang H; Luan F; Zhang R; Liu M; Hu Z; Fan B
    J Mol Graph Model; 2007 Jul; 26(1):246-54. PubMed ID: 17275373
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prediction of peptide binding to major histocompatibility complex class II molecules through use of boosted fuzzy classifier with SWEEP operator method.
    Takahashi H; Honda H
    J Biosci Bioeng; 2006 Feb; 101(2):137-41. PubMed ID: 16569609
    [TBL] [Abstract][Full Text] [Related]  

  • 13. POPI: predicting immunogenicity of MHC class I binding peptides by mining informative physicochemical properties.
    Tung CW; Ho SY
    Bioinformatics; 2007 Apr; 23(8):942-9. PubMed ID: 17384427
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Application of an artificial neural network to predict specific class I MHC binding peptide sequences.
    Milik M; Sauer D; Brunmark AP; Yuan L; Vitiello A; Jackson MR; Peterson PA; Skolnick J; Glass CA
    Nat Biotechnol; 1998 Aug; 16(8):753-6. PubMed ID: 9702774
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Class II HLA-peptide binding prediction using structural principles.
    Mohanapriya A; Lulu S; Kayathri R; Kangueane P
    Hum Immunol; 2009 Mar; 70(3):159-69. PubMed ID: 19187794
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Prediction of peptide-MHC binding using profiles.
    Reche PA; Reinherz EL
    Methods Mol Biol; 2007; 409():185-200. PubMed ID: 18450001
    [TBL] [Abstract][Full Text] [Related]  

  • 18. T cell responses to bluetongue virus are directed against multiple and identical CD4+ and CD8+ T cell epitopes from the VP7 core protein in mouse and sheep.
    Rojas JM; Rodríguez-Calvo T; Peña L; Sevilla N
    Vaccine; 2011 Sep; 29(40):6848-57. PubMed ID: 21807057
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Building MHC class II epitope predictor using machine learning approaches.
    Eng LP; Tan TW; Tong JC
    Methods Mol Biol; 2015; 1268():67-73. PubMed ID: 25555721
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 5.