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Journal Abstract Search

317 related items for PubMed ID: 26020952

  • 1. Accurate prediction of immunogenic T-cell epitopes from epitope sequences using the genetic algorithm-based ensemble learning.
    Zhang W, Niu Y, Zou H, Luo L, Liu Q, Wu W.
    PLoS One; 2015; 10(5):e0128194. PubMed ID: 26020952
    [Abstract] [Full Text] [Related]

  • 2. Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design.
    Moghram BA, Nabil E, Badr A.
    Comput Methods Programs Biomed; 2018 Jan; 153():161-170. PubMed ID: 29157448
    [Abstract] [Full Text] [Related]

  • 3. INeo-Epp: A Novel T-Cell HLA Class-I Immunogenicity or Neoantigenic Epitope Prediction Method Based on Sequence-Related Amino Acid Features.
    Wang G, Wan H, Jian X, Li Y, Ouyang J, Tan X, Zhao Y, Lin Y, Xie L.
    Biomed Res Int; 2020 Jan; 2020():5798356. PubMed ID: 32626747
    [Abstract] [Full Text] [Related]

  • 4. Ensemble Technique for Prediction of T-cell Mycobacterium tuberculosis Epitopes.
    Khanna D, Rana PS.
    Interdiscip Sci; 2019 Dec; 11(4):611-627. PubMed ID: 30406342
    [Abstract] [Full Text] [Related]

  • 5. Harnessing Computational Biology for Exact Linear B-Cell Epitope Prediction: A Novel Amino Acid Composition-Based Feature Descriptor.
    Saravanan V, Gautham N.
    OMICS; 2015 Oct; 19(10):648-58. PubMed ID: 26406767
    [Abstract] [Full Text] [Related]

  • 6. An ensemble method for prediction of conformational B-cell epitopes from antigen sequences.
    Zheng W, Zhang C, Hanlon M, Ruan J, Gao J.
    Comput Biol Chem; 2014 Apr; 49():51-8. PubMed ID: 24607818
    [Abstract] [Full Text] [Related]

  • 7. Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space.
    Ogishi M, Yotsuyanagi H.
    Front Immunol; 2019 Apr; 10():827. PubMed ID: 31057550
    [Abstract] [Full Text] [Related]

  • 8. PAAQD: Predicting immunogenicity of MHC class I binding peptides using amino acid pairwise contact potentials and quantum topological molecular similarity descriptors.
    Saethang T, Hirose O, Kimkong I, Tran VA, Dang XT, Nguyen LA, Le TK, Kubo M, Yamada Y, Satou K.
    J Immunol Methods; 2013 Jan 31; 387(1-2):293-302. PubMed ID: 23058674
    [Abstract] [Full Text] [Related]

  • 9. Prediction of T-cell epitopes using biosupport vector machines.
    Yang ZR, Johnson FC.
    J Chem Inf Model; 2005 Jan 31; 45(5):1424-8. PubMed ID: 16180919
    [Abstract] [Full Text] [Related]

  • 10. Machine Learning-Based Ensemble Model for Zika Virus T-Cell Epitope Prediction.
    Bukhari SNH, Jain A, Haq E, Khder MA, Neware R, Bhola J, Lari Najafi M.
    J Healthc Eng; 2021 Jan 31; 2021():9591670. PubMed ID: 34631001
    [Abstract] [Full Text] [Related]

  • 11. Using random forest to classify T-cell epitopes based on amino acid properties and molecular features.
    Huang JH, Xie HL, Yan J, Lu HM, Xu QS, Liang YZ.
    Anal Chim Acta; 2013 Dec 04; 804():70-5. PubMed ID: 24267065
    [Abstract] [Full Text] [Related]

  • 12. Engineering immunogenic consensus T helper epitopes for a cross-clade HIV vaccine.
    De Groot AS, Bishop EA, Khan B, Lally M, Marcon L, Franco J, Mayer KH, Carpenter CC, Martin W.
    Methods; 2004 Dec 04; 34(4):476-87. PubMed ID: 15542374
    [Abstract] [Full Text] [Related]

  • 13. POPISK: T-cell reactivity prediction using support vector machines and string kernels.
    Tung CW, Ziehm M, Kämper A, Kohlbacher O, Ho SY.
    BMC Bioinformatics; 2011 Nov 15; 12():446. PubMed ID: 22085524
    [Abstract] [Full Text] [Related]

  • 14. In silico analysis of MHC-I restricted epitopes of Chikungunya virus proteins: Implication in understanding anti-CHIKV CD8(+) T cell response and advancement of epitope based immunotherapy for CHIKV infection.
    Pratheek BM, Suryawanshi AR, Chattopadhyay S, Chattopadhyay S.
    Infect Genet Evol; 2015 Apr 15; 31():118-26. PubMed ID: 25643869
    [Abstract] [Full Text] [Related]

  • 15. POPI: predicting immunogenicity of MHC class I binding peptides by mining informative physicochemical properties.
    Tung CW, Ho SY.
    Bioinformatics; 2007 Apr 15; 23(8):942-9. PubMed ID: 17384427
    [Abstract] [Full Text] [Related]

  • 16. Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning.
    Zhang W, Niu Y, Xiong Y, Zhao M, Yu R, Liu J.
    PLoS One; 2012 Apr 15; 7(8):e43575. PubMed ID: 22927994
    [Abstract] [Full Text] [Related]

  • 17. iBCE-EL: A New Ensemble Learning Framework for Improved Linear B-Cell Epitope Prediction.
    Manavalan B, Govindaraj RG, Shin TH, Kim MO, Lee G.
    Front Immunol; 2018 Apr 15; 9():1695. PubMed ID: 30100904
    [Abstract] [Full Text] [Related]

  • 18. SEPIa, a knowledge-driven algorithm for predicting conformational B-cell epitopes from the amino acid sequence.
    Dalkas GA, Rooman M.
    BMC Bioinformatics; 2017 Feb 10; 18(1):95. PubMed ID: 28183272
    [Abstract] [Full Text] [Related]

  • 19. An assessment on epitope prediction methods for protozoa genomes.
    Resende DM, Rezende AM, Oliveira NJ, Batista IC, Corrêa-Oliveira R, Reis AB, Ruiz JC.
    BMC Bioinformatics; 2012 Nov 21; 13():309. PubMed ID: 23170965
    [Abstract] [Full Text] [Related]

  • 20. A novel multi-epitope peptide vaccine against cancer: an in silico approach.
    Nezafat N, Ghasemi Y, Javadi G, Khoshnoud MJ, Omidinia E.
    J Theor Biol; 2014 May 21; 349():121-34. PubMed ID: 24512916
    [Abstract] [Full Text] [Related]

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