117 related articles for article (PubMed ID: 21413918)
1. 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]
2. 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]
3. Characterization of the binding profile of peptide to transporter associated with antigen processing (TAP) using Gaussian process regression.
Ren Y; Wu B; Pan Y; Lv F; Kong X; Luo X; Li Y; Yang Q
Comput Biol Med; 2011 Sep; 41(9):865-70. PubMed ID: 21816395
[TBL] [Abstract][Full Text] [Related]
4. Gaussian process: an alternative approach for QSAM modeling of peptides.
Zhou P; Chen X; Wu Y; Shang Z
Amino Acids; 2010 Jan; 38(1):199-212. PubMed ID: 19123053
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes.
Zhao W; Sher X
PLoS Comput Biol; 2018 Nov; 14(11):e1006457. PubMed ID: 30408041
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Support vector machine-based prediction of MHC-binding peptides.
Dönnes P
Methods Mol Biol; 2007; 409():273-82. PubMed ID: 18450007
[TBL] [Abstract][Full Text] [Related]
9. Shift-invariant adaptive double threading: learning MHC II-peptide binding.
Zaitlen N; Reyes-Gomez M; Heckerman D; Jojic N
J Comput Biol; 2008 Sep; 15(7):927-42. PubMed ID: 18771399
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. 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]
13. Modeling and prediction of binding affinities between the human amphiphysin SH3 domain and its peptide ligands using genetic algorithm-Gaussian processes.
Zhou P; Tian F; Chen X; Shang Z
Biopolymers; 2008; 90(6):792-802. PubMed ID: 18814309
[TBL] [Abstract][Full Text] [Related]
14. Prediction of MHC-peptide binding: a systematic and comprehensive overview.
Lafuente EM; Reche PA
Curr Pharm Des; 2009; 15(28):3209-20. PubMed ID: 19860671
[TBL] [Abstract][Full Text] [Related]
15. Modeling protein-peptide recognition based on classical quantitative structure-affinity relationship approach: implication for proteome-wide inference of peptide-mediated interactions.
Zhou Y; Ni Z; Chen K; Liu H; Chen L; Lian C; Yan L
Protein J; 2013 Oct; 32(7):568-78. PubMed ID: 24150505
[TBL] [Abstract][Full Text] [Related]
16. Modeling and prediction of peptide drift times in ion mobility spectrometry using sequence-based and structure-based approaches.
Zhang Y; Jin Q; Wang S; Ren R
Comput Biol Med; 2011 May; 41(5):272-7. PubMed ID: 21439562
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Application of genetic search in derivation of matrix models of peptide binding to MHC molecules.
Brusic V; Schönbach C; Takiguchi M; Ciesielski V; Harrison LC
Proc Int Conf Intell Syst Mol Biol; 1997; 5():75-83. PubMed ID: 9322018
[TBL] [Abstract][Full Text] [Related]
19. Predicting liquid chromatographic retention times of peptides from the Drosophila melanogaster proteome by machine learning approaches.
Tian F; Yang L; Lv F; Zhou P
Anal Chim Acta; 2009 Jun; 644(1-2):10-6. PubMed ID: 19463555
[TBL] [Abstract][Full Text] [Related]
20. Implementing the modular MHC model for predicting peptide binding.
DeLuca DS; Blasczyk R
Methods Mol Biol; 2007; 409():261-71. PubMed ID: 18450006
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
