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Journal Abstract Search

1271 related items for PubMed ID: 18450004

  • 1. 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
    [Abstract] [Full Text] [Related]

  • 2. New horizons in mouse immunoinformatics: reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity.
    Hattotuwagama CK, Guan P, Doytchinova IA, Flower DR.
    Org Biomol Chem; 2004 Nov 21; 2(22):3274-83. PubMed ID: 15534705
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  • 3. In silico prediction of peptide binding affinity to class I mouse major histocompatibility complexes: a comparative molecular similarity index analysis (CoMSIA) study.
    Hattotuwagama CK, Doytchinova IA, Flower DR.
    J Chem Inf Model; 2005 Nov 21; 45(5):1415-23. PubMed ID: 16180918
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  • 4. 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 Nov 21; 46(3):1491-502. PubMed ID: 16711768
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  • 5. Static energy analysis of MHC class I and class II peptide-binding affinity.
    Davies MN, Flower DR.
    Methods Mol Biol; 2007 Nov 21; 409():309-20. PubMed ID: 18450011
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  • 6. In silico prediction of peptide-MHC binding affinity using SVRMHC.
    Liu W, Wan J, Meng X, Flower DR, Li T.
    Methods Mol Biol; 2007 Nov 21; 409():283-91. PubMed ID: 18450008
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  • 9. Quantitative structure-activity relationships and the prediction of MHC supermotifs.
    Doytchinova IA, Guan P, Flower DR.
    Methods; 2004 Dec 21; 34(4):444-53. PubMed ID: 15542370
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  • 10. 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 22; 19(17):2263-70. PubMed ID: 14630655
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  • 11. Computational vaccinology: quantitative approaches.
    Flower DR, McSparron H, Blythe MJ, Zygouri C, Taylor D, Guan P, Wan S, Coveney PV, Walshe V, Borrow P, Doytchinova IA.
    Novartis Found Symp; 2003 Nov 22; 254():102-20; discussion 120-5, 216-22, 250-2. PubMed ID: 14712934
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  • 12. Predicting the MHC-peptide affinity using some interactive-type molecular descriptors and QSAR models.
    Lin TH.
    Methods Mol Biol; 2007 Nov 22; 409():247-60. PubMed ID: 18450005
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  • 13. EpiTOP--a proteochemometric tool for MHC class II binding prediction.
    Dimitrov I, Garnev P, Flower DR, Doytchinova I.
    Bioinformatics; 2010 Aug 15; 26(16):2066-8. PubMed ID: 20576624
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  • 16. An iterative approach to class II predictions.
    Mallios RR.
    Methods Mol Biol; 2007 Aug 15; 409():341-53. PubMed ID: 18450013
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  • 17. BiodMHC: an online server for the prediction of MHC class II-peptide binding affinity.
    Wang L, Pan D, Hu X, Xiao J, Gao Y, Zhang H, Zhang Y, Liu J, Zhu S.
    J Genet Genomics; 2009 May 15; 36(5):289-96. PubMed ID: 19447377
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  • 18. Integrating in silico and in vitro analysis of peptide binding affinity to HLA-Cw*0102: a bioinformatic approach to the prediction of new epitopes.
    Walshe VA, Hattotuwagama CK, Doytchinova IA, Wong M, Macdonald IK, Mulder A, Claas FH, Pellegrino P, Turner J, Williams I, Turnbull EL, Borrow P, Flower DR.
    PLoS One; 2009 Nov 30; 4(11):e8095. PubMed ID: 19956609
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  • 19. Class II HLA-peptide binding prediction using structural principles.
    Mohanapriya A, Lulu S, Kayathri R, Kangueane P.
    Hum Immunol; 2009 Mar 30; 70(3):159-69. PubMed ID: 19187794
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  • 20. Toward the quantitative prediction of T-cell epitopes: QSAR studies on peptides having affinity with the class I MHC molecular HLA-A*0201.
    Zhihua L, Yuzhang W, Bo Z, Bing N, Li W.
    J Comput Biol; 2004 Mar 30; 11(4):683-94. PubMed ID: 15579238
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