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.
174 related articles for article (PubMed ID: 25555721)
1. 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]
2. 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]
3. 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]
4. 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]
5. T-Cell Epitope Prediction of Chikungunya Virus. Eng CL; Tan TW; Tong JC Methods Mol Biol; 2016; 1426():201-7. PubMed ID: 27233273 [TBL] [Abstract][Full Text] [Related]
6. Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification. Andreatta M; Karosiene E; Rasmussen M; Stryhn A; Buus S; Nielsen M Immunogenetics; 2015 Nov; 67(11-12):641-50. PubMed ID: 26416257 [TBL] [Abstract][Full Text] [Related]
7. Predicting promiscuous antigenic T cell epitopes of Mycobacterium tuberculosis mymA operon proteins binding to MHC Class I and Class II molecules. Saraav I; Pandey K; Sharma M; Singh S; Dutta P; Bhardwaj A; Sharma S Infect Genet Evol; 2016 Oct; 44():182-189. PubMed ID: 27389362 [TBL] [Abstract][Full Text] [Related]
8. Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles. Reche PA; Glutting JP; Zhang H; Reinherz EL Immunogenetics; 2004 Sep; 56(6):405-19. PubMed ID: 15349703 [TBL] [Abstract][Full Text] [Related]
9. Introducing of an integrated artificial neural network and Chou's pseudo amino acid composition approach for computational epitope-mapping of Crimean-Congo haemorrhagic fever virus antigens. Nosrati M; Mohabatkar H; Behbahani M Int Immunopharmacol; 2020 Jan; 78():106020. PubMed ID: 31776090 [TBL] [Abstract][Full Text] [Related]
10. 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]
11. NNAlign_MA; MHC Peptidome Deconvolution for Accurate MHC Binding Motif Characterization and Improved T-cell Epitope Predictions. Alvarez B; Reynisson B; Barra C; Buus S; Ternette N; Connelley T; Andreatta M; Nielsen M Mol Cell Proteomics; 2019 Dec; 18(12):2459-2477. PubMed ID: 31578220 [TBL] [Abstract][Full Text] [Related]
13. EpiTOP--a proteochemometric tool for MHC class II binding prediction. Dimitrov I; Garnev P; Flower DR; Doytchinova I Bioinformatics; 2010 Aug; 26(16):2066-8. PubMed ID: 20576624 [TBL] [Abstract][Full Text] [Related]
14. Predictor for the effect of amino acid composition on CD4+ T cell epitopes preprocessing. Hoze E; Tsaban L; Maman Y; Louzoun Y J Immunol Methods; 2013 May; 391(1-2):163-73. PubMed ID: 23481624 [TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. 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]
18. Designing of interferon-gamma inducing MHC class-II binders. Dhanda SK; Vir P; Raghava GP Biol Direct; 2013 Dec; 8():30. PubMed ID: 24304645 [TBL] [Abstract][Full Text] [Related]
19. Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network. Brusic V; Rudy G; Honeyman G; Hammer J; Harrison L Bioinformatics; 1998; 14(2):121-30. PubMed ID: 9545443 [TBL] [Abstract][Full Text] [Related]
20. 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] [Next] [New Search]