BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

159 related articles for article (PubMed ID: 20351933)

  • 1. Support vector machine-based mucin-type o-linked glycosylation site prediction using enhanced sequence feature encoding.
    Torii M; Liu H; Hu ZZ
    AMIA Annu Symp Proc; 2009 Nov; 2009():640-4. PubMed ID: 20351933
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Prediction of mucin-type O-glycosylation sites in mammalian proteins using the composition of k-spaced amino acid pairs.
    Chen YZ; Tang YR; Sheng ZY; Zhang Z
    BMC Bioinformatics; 2008 Feb; 9():101. PubMed ID: 18282281
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Positive-unlabelled learning of glycosylation sites in the human proteome.
    Li F; Zhang Y; Purcell AW; Webb GI; Chou KC; Lithgow T; Li C; Song J
    BMC Bioinformatics; 2019 Mar; 20(1):112. PubMed ID: 30841845
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Glycosylation site prediction using ensembles of Support Vector Machine classifiers.
    Caragea C; Sinapov J; Silvescu A; Dobbs D; Honavar V
    BMC Bioinformatics; 2007 Nov; 8():438. PubMed ID: 17996106
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Prediction of posttranslational modification sites from amino acid sequences with kernel methods.
    Xu Y; Wang X; Wang Y; Tian Y; Shao X; Wu LY; Deng N
    J Theor Biol; 2014 Mar; 344():78-87. PubMed ID: 24291233
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins.
    Taherzadeh G; Campbell M; Zhou Y
    Methods Mol Biol; 2022; 2499():177-186. PubMed ID: 35696081
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prediction of O-glycosylation sites based on multi-scale composition of amino acids and feature selection.
    Chen Y; Zhou W; Wang H; Yuan Z
    Med Biol Eng Comput; 2015 Jun; 53(6):535-44. PubMed ID: 25752770
    [TBL] [Abstract][Full Text] [Related]  

  • 8. O-glycosylation site prediction for
    Zhu Y; Yin S; Zheng J; Shi Y; Jia C
    J Bioinform Comput Biol; 2022 Feb; 20(1):2150029. PubMed ID: 34806952
    [TBL] [Abstract][Full Text] [Related]  

  • 9. GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome.
    Li F; Li C; Wang M; Webb GI; Zhang Y; Whisstock JC; Song J
    Bioinformatics; 2015 May; 31(9):1411-9. PubMed ID: 25568279
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Prediction of protein binding sites in protein structures using hidden Markov support vector machine.
    Liu B; Wang X; Lin L; Tang B; Dong Q; Wang X
    BMC Bioinformatics; 2009 Nov; 10():381. PubMed ID: 19925685
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Predicting O-glycosylation sites in mammalian proteins by using SVMs.
    Li S; Liu B; Zeng R; Cai Y; Li Y
    Comput Biol Chem; 2006 Jun; 30(3):203-8. PubMed ID: 16731044
    [TBL] [Abstract][Full Text] [Related]  

  • 12. O-GlyThr: Prediction of human O-linked threonine glycosites using multi-feature fusion.
    Tang H; Tang Q; Zhang Q; Feng P
    Int J Biol Macromol; 2023 Jul; 242(Pt 2):124761. PubMed ID: 37156312
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Bio-support vector machines for computational proteomics.
    Yang ZR; Chou KC
    Bioinformatics; 2004 Mar; 20(5):735-41. PubMed ID: 14751989
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Glypre: In Silico Prediction of Protein Glycation Sites by Fusing Multiple Features and Support Vector Machine.
    Zhao X; Zhao X; Bao L; Zhang Y; Dai J; Yin M
    Molecules; 2017 Nov; 22(11):. PubMed ID: 29099805
    [TBL] [Abstract][Full Text] [Related]  

  • 15. HMMpTM: improving transmembrane protein topology prediction using phosphorylation and glycosylation site prediction.
    Tsaousis GN; Bagos PG; Hamodrakas SJ
    Biochim Biophys Acta; 2014 Feb; 1844(2):316-22. PubMed ID: 24225132
    [TBL] [Abstract][Full Text] [Related]  

  • 16. NetOglyc: prediction of mucin type O-glycosylation sites based on sequence context and surface accessibility.
    Hansen JE; Lund O; Tolstrup N; Gooley AA; Williams KL; Brunak S
    Glycoconj J; 1998 Feb; 15(2):115-30. PubMed ID: 9557871
    [TBL] [Abstract][Full Text] [Related]  

  • 17. PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features.
    Nilamyani AN; Auliah FN; Moni MA; Shoombuatong W; Hasan MM; Kurata H
    Int J Mol Sci; 2021 Mar; 22(5):. PubMed ID: 33800121
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Lysine acetylation sites prediction using an ensemble of support vector machine classifiers.
    Xu Y; Wang XB; Ding J; Wu LY; Deng NY
    J Theor Biol; 2010 May; 264(1):130-5. PubMed ID: 20085770
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Computational methods for ubiquitination site prediction using physicochemical properties of protein sequences.
    Cai B; Jiang X
    BMC Bioinformatics; 2016 Mar; 17():116. PubMed ID: 26940649
    [TBL] [Abstract][Full Text] [Related]  

  • 20. iDPGK: characterization and identification of lysine phosphoglycerylation sites based on sequence-based features.
    Huang KY; Hung FY; Kao HJ; Lau HH; Weng SL
    BMC Bioinformatics; 2020 Dec; 21(1):568. PubMed ID: 33297954
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.