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.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

261 related articles for article (PubMed ID: 23484214)

  • 1. Prediction of B-cell epitopes using evolutionary information and propensity scales.
    Lin SY; Cheng CW; Su EC
    BMC Bioinformatics; 2013; 14 Suppl 2(Suppl 2):S10. PubMed ID: 23484214
    [TBL] [Abstract][Full Text] [Related]  

  • 2. BepFAMN: A Method for Linear B-Cell Epitope Predictions Based on Fuzzy-ARTMAP Artificial Neural Network.
    La Marca AF; Lopes RDS; Lotufo ADP; Bartholomeu DC; Minussi CR
    Sensors (Basel); 2022 May; 22(11):. PubMed ID: 35684648
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Prediction of conformational B-cell epitopes from 3D structures by random forests with a distance-based feature.
    Zhang W; Xiong Y; Zhao M; Zou H; Ye X; Liu J
    BMC Bioinformatics; 2011 Aug; 12():341. PubMed ID: 21846404
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification.
    Wang HW; Lin YC; Pai TW; Chang HT
    J Biomed Biotechnol; 2011; 2011():432830. PubMed ID: 21876642
    [TBL] [Abstract][Full Text] [Related]  

  • 6. SVMTriP: A Method to Predict B-Cell Linear Antigenic Epitopes.
    Yao B; Zheng D; Liang S; Zhang C
    Methods Mol Biol; 2020; 2131():299-307. PubMed ID: 32162263
    [TBL] [Abstract][Full Text] [Related]  

  • 7. epitope3D: a machine learning method for conformational B-cell epitope prediction.
    da Silva BM; Myung Y; Ascher DB; Pires DEV
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34676398
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Classification epitopes in groups based on their protein family.
    Kozlova E; Viart B; de Avila R; Felicori L; Chavez-Olortegui C
    BMC Bioinformatics; 2015; 16 Suppl 19(Suppl 19):S7. PubMed ID: 26696329
    [TBL] [Abstract][Full Text] [Related]  

  • 9. CrystalM: A Multi-View Fusion Approach for Protein Crystallization Prediction.
    Wang Y; Ding Y; Tang J; Dai Y; Guo F
    IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(1):325-335. PubMed ID: 31027046
    [TBL] [Abstract][Full Text] [Related]  

  • 10. SVMTriP: a method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity.
    Yao B; Zhang L; Liang S; Zhang C
    PLoS One; 2012; 7(9):e45152. PubMed ID: 22984622
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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; 9():1695. PubMed ID: 30100904
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Predicting RNA-binding sites of proteins using support vector machines and evolutionary information.
    Cheng CW; Su EC; Hwang JK; Sung TY; Hsu WL
    BMC Bioinformatics; 2008 Dec; 9 Suppl 12(Suppl 12):S6. PubMed ID: 19091029
    [TBL] [Abstract][Full Text] [Related]  

  • 13. EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression.
    Lian Y; Ge M; Pan XM
    BMC Bioinformatics; 2014 Dec; 15(1):414. PubMed ID: 25523327
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Improvement in prediction of antigenic epitopes using stacked generalisation: an ensemble approach.
    Khanna D; Rana PS
    IET Syst Biol; 2020 Feb; 14(1):1-7. PubMed ID: 31931475
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Using random forest to classify linear B-cell epitopes based on amino acid properties and molecular features.
    Huang JH; Wen M; Tang LJ; Xie HL; Fu L; Liang YZ; Lu HM
    Biochimie; 2014 Aug; 103():1-6. PubMed ID: 24721579
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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; 7(8):e43575. PubMed ID: 22927994
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Prediction of nuclear proteins using nuclear translocation signals proposed by probabilistic latent semantic indexing.
    Su EC; Chang JM; Cheng CW; Sung TY; Hsu WL
    BMC Bioinformatics; 2012; 13 Suppl 17(Suppl 17):S13. PubMed ID: 23282098
    [TBL] [Abstract][Full Text] [Related]  

  • 19. EPSVR and EPMeta: prediction of antigenic epitopes using support vector regression and multiple server results.
    Liang S; Zheng D; Standley DM; Yao B; Zacharias M; Zhang C
    BMC Bioinformatics; 2010 Jul; 11():381. PubMed ID: 20637083
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 14.