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 *

226 related articles for article (PubMed ID: 23276225)

  • 1. Prediction of methylation sites using the composition of K-spaced amino acid pairs.
    Zhang W; Xu X; Yin M; Luo N; Zhang J; Wang J
    Protein Pept Lett; 2013 Aug; 20(8):911-7. PubMed ID: 23276225
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou׳s general PseAAC.
    Ju Z; Cao JZ; Gu H
    J Theor Biol; 2016 May; 397():145-50. PubMed ID: 26908349
    [TBL] [Abstract][Full Text] [Related]  

  • 3. iLM-2L: A two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou׳s general PseAAC.
    Ju Z; Cao JZ; Gu H
    J Theor Biol; 2015 Nov; 385():50-7. PubMed ID: 26254214
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Prediction of protein N-formylation using the composition of k-spaced amino acid pairs.
    Ju Z; Cao JZ
    Anal Biochem; 2017 Oct; 534():40-45. PubMed ID: 28709899
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Prediction of lysine crotonylation sites by incorporating the composition of k-spaced amino acid pairs into Chou's general PseAAC.
    Ju Z; He JJ
    J Mol Graph Model; 2017 Oct; 77():200-204. PubMed ID: 28886434
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Prediction of protein phosphorylation sites by using the composition of k-spaced amino acid pairs.
    Zhao X; Zhang W; Xu X; Ma Z; Yin M
    PLoS One; 2012; 7(10):e46302. PubMed ID: 23110047
    [TBL] [Abstract][Full Text] [Related]  

  • 7. hCKSAAP_UbSite: improved prediction of human ubiquitination sites by exploiting amino acid pattern and properties.
    Chen Z; Zhou Y; Song J; Zhang Z
    Biochim Biophys Acta; 2013 Aug; 1834(8):1461-7. PubMed ID: 23603789
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Prediction of ubiquitination sites by using the composition of k-spaced amino acid pairs.
    Chen Z; Chen YZ; Wang XF; Wang C; Yan RX; Zhang Z
    PLoS One; 2011; 6(7):e22930. PubMed ID: 21829559
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Identification of protein methylation sites by coupling improved ant colony optimization algorithm and support vector machine.
    Li ZC; Zhou X; Dai Z; Zou XY
    Anal Chim Acta; 2011 Oct; 703(2):163-71. PubMed ID: 21889630
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites.
    Huang KY; Kao HJ; Hsu JB; Weng SL; Lee TY
    BMC Bioinformatics; 2019 Feb; 19(Suppl 13):384. PubMed ID: 30717647
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Computational prediction of methylation types of covalently modified lysine and arginine residues in proteins.
    Deng W; Wang Y; Ma L; Zhang Y; Ullah S; Xue Y
    Brief Bioinform; 2017 Jul; 18(4):647-658. PubMed ID: 27241573
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prediction and analysis of protein methylarginine and methyllysine based on Multisequence features.
    Hu LL; Li Z; Wang K; Niu S; Shi XH; Cai YD; Li HP
    Biopolymers; 2011 Nov; 95(11):763-71. PubMed ID: 21544797
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Two-Level Protein Methylation Prediction using structure model-based features.
    Zheng W; Wuyun Q; Cheng M; Hu G; Zhang Y
    Sci Rep; 2020 Apr; 10(1):6008. PubMed ID: 32265459
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou's 5-steps rule and general pseudo components.
    Ju Z; Wang SY
    Genomics; 2020 Jan; 112(1):859-866. PubMed ID: 31175975
    [TBL] [Abstract][Full Text] [Related]  

  • 15. MeMo: a web tool for prediction of protein methylation modifications.
    Chen H; Xue Y; Huang N; Yao X; Sun Z
    Nucleic Acids Res; 2006 Jul; 34(Web Server issue):W249-53. PubMed ID: 16845004
    [TBL] [Abstract][Full Text] [Related]  

  • 16. PRMxAI: protein arginine methylation sites prediction based on amino acid spatial distribution using explainable artificial intelligence.
    Khandelwal M; Rout RK
    BMC Bioinformatics; 2023 Oct; 24(1):376. PubMed ID: 37794362
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique.
    Zhao X; Ning Q; Chai H; Ma Z
    J Theor Biol; 2015 Jun; 374():60-5. PubMed ID: 25843215
    [TBL] [Abstract][Full Text] [Related]  

  • 18. PLMLA: prediction of lysine methylation and lysine acetylation by combining multiple features.
    Shi SP; Qiu JD; Sun XY; Suo SB; Huang SY; Liang RP
    Mol Biosyst; 2012 Apr; 8(5):1520-7. PubMed ID: 22402705
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Prediction of protein methylation sites using conditional random field.
    Xu Y; Ding J; Huang Q; Deng NY
    Protein Pept Lett; 2013 Jan; 20(1):71-7. PubMed ID: 22789108
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou's general pseudo amino acid composition.
    Ju Z; Wang SY
    Gene; 2018 Jul; 664():78-83. PubMed ID: 29694908
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 12.