BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

105 related articles for article (PubMed ID: 18450007)

  • 41. Building a meta-predictor for MHC class II-binding peptides.
    Huang L; Karpenko O; Murugan N; Dai Y
    Methods Mol Biol; 2007; 409():355-64. PubMed ID: 18450014
    [TBL] [Abstract][Full Text] [Related]  

  • 42. HistoCheck. Evaluating structural and functional MHC similarities.
    DeLuca DS; Blasczyk R
    Methods Mol Biol; 2007; 409():395-405. PubMed ID: 18450018
    [TBL] [Abstract][Full Text] [Related]  

  • 43. Hybrid biogeography based simultaneous feature selection and MHC class I peptide binding prediction using support vector machines and random forests.
    Srivastava A; Ghosh S; Anantharaman N; Jayaraman VK
    J Immunol Methods; 2013 Jan; 387(1-2):284-92. PubMed ID: 23058675
    [TBL] [Abstract][Full Text] [Related]  

  • 44. NIEluter: Predicting peptides eluted from HLA class I molecules.
    Tang Q; Nie F; Kang J; Ding H; Zhou P; Huang J
    J Immunol Methods; 2015 Jul; 422():22-7. PubMed ID: 25862605
    [TBL] [Abstract][Full Text] [Related]  

  • 45. Gaussian process: a promising approach for the modeling and prediction of Peptide binding affinity to MHC proteins.
    Ren Y; Chen X; Feng M; Wang Q; Zhou P
    Protein Pept Lett; 2011 Jul; 18(7):670-8. PubMed ID: 21413918
    [TBL] [Abstract][Full Text] [Related]  

  • 46. Application of support vector machines for T-cell epitopes prediction.
    Zhao Y; Pinilla C; Valmori D; Martin R; Simon R
    Bioinformatics; 2003 Oct; 19(15):1978-84. PubMed ID: 14555632
    [TBL] [Abstract][Full Text] [Related]  

  • 47. Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models.
    Mamitsuka H
    Proteins; 1998 Dec; 33(4):460-74. PubMed ID: 9849933
    [TBL] [Abstract][Full Text] [Related]  

  • 48. SVM and SVR-based MHC-binding prediction using a mathematical presentation of peptide sequences.
    Jandrlić DR
    Comput Biol Chem; 2016 Dec; 65():117-127. PubMed ID: 27816828
    [TBL] [Abstract][Full Text] [Related]  

  • 49. PeptX: using genetic algorithms to optimize peptides for MHC binding.
    Knapp B; Giczi V; Ribarics R; Schreiner W
    BMC Bioinformatics; 2011 Jun; 12():241. PubMed ID: 21679477
    [TBL] [Abstract][Full Text] [Related]  

  • 50. sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides.
    Luo H; Ye H; Ng HW; Sakkiah S; Mendrick DL; Hong H
    Sci Rep; 2016 Aug; 6():32115. PubMed ID: 27558848
    [TBL] [Abstract][Full Text] [Related]  

  • 51. MetaMHCpan, A Meta Approach for Pan-Specific MHC Peptide Binding Prediction.
    Xu Y; Luo C; Mamitsuka H; Zhu S
    Methods Mol Biol; 2016; 1404():753-760. PubMed ID: 27076335
    [TBL] [Abstract][Full Text] [Related]  

  • 52. Machine learning competition in immunology - Prediction of HLA class I binding peptides.
    Zhang GL; Ansari HR; Bradley P; Cawley GC; Hertz T; Hu X; Jojic N; Kim Y; Kohlbacher O; Lund O; Lundegaard C; Magaret CA; Nielsen M; Papadopoulos H; Raghava GP; Tal VS; Xue LC; Yanover C; Zhu S; Rock MT; Crowe JE; Panayiotou C; Polycarpou MM; Duch W; Brusic V
    J Immunol Methods; 2011 Nov; 374(1-2):1-4. PubMed ID: 21986107
    [No Abstract]   [Full Text] [Related]  

  • 53. Accurate prediction of major histocompatibility complex class II epitopes by sparse representation via ℓ 1-minimization.
    Aguilar-Bonavides C; Sanchez-Arias R; Lanzas C
    BioData Min; 2014; 7():23. PubMed ID: 25392716
    [TBL] [Abstract][Full Text] [Related]  

  • 54. High-order neural networks and kernel methods for peptide-MHC binding prediction.
    Kuksa PP; Min MR; Dugar R; Gerstein M
    Bioinformatics; 2015 Nov; 31(22):3600-7. PubMed ID: 26206306
    [TBL] [Abstract][Full Text] [Related]  

  • 55. Support vector machine based prediction of glutathione S-transferase proteins.
    Mishra NK; Kumar M; Raghava GP
    Protein Pept Lett; 2007; 14(6):575-80. PubMed ID: 17627599
    [TBL] [Abstract][Full Text] [Related]  

  • 56. Quantification of Uncertainty in Peptide-MHC Binding Prediction Improves High-Affinity Peptide Selection for Therapeutic Design.
    Zeng H; Gifford DK
    Cell Syst; 2019 Aug; 9(2):159-166.e3. PubMed ID: 31176619
    [TBL] [Abstract][Full Text] [Related]  

  • 57. 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]  

  • 58. Scrutinizing MHC-I binding peptides and their limits of variation.
    Koch CP; Perna AM; Pillong M; Todoroff NK; Wrede P; Folkers G; Hiss JA; Schneider G
    PLoS Comput Biol; 2013; 9(6):e1003088. PubMed ID: 23754940
    [TBL] [Abstract][Full Text] [Related]  

  • 59. Computational prediction of therapeutic peptides based on graph index.
    Xu C; Ge L; Zhang Y; Dehmer M; Gutman I
    J Biomed Inform; 2017 Nov; 75():63-69. PubMed ID: 28958485
    [TBL] [Abstract][Full Text] [Related]  

  • 60. Structure-based clustering of major histocompatibility complex (MHC) proteins for broad-based T-cell vaccine design.
    Tong JC; Tan TW; Ranganathan S
    Methods Mol Biol; 2014; 1184():503-11. PubMed ID: 25048142
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 6.