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 *

219 related articles for article (PubMed ID: 29994125)

  • 1. iGlu-Lys: A Predictor for Lysine Glutarylation Through Amino Acid Pair Order Features.
    Xu Y; Yang Y; Ding J; Li C
    IEEE Trans Nanobioscience; 2018 Oct; 17(4):394-401. PubMed ID: 29994125
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. Prediction of lysine glutarylation sites by maximum relevance minimum redundancy feature selection.
    Ju Z; He JJ
    Anal Biochem; 2018 Jun; 550():1-7. PubMed ID: 29641975
    [TBL] [Abstract][Full Text] [Related]  

  • 4. DeepDN_iGlu: prediction of lysine glutarylation sites based on attention residual learning method and DenseNet.
    Jia J; Sun M; Wu G; Qiu W
    Math Biosci Eng; 2023 Jan; 20(2):2815-2830. PubMed ID: 36899559
    [TBL] [Abstract][Full Text] [Related]  

  • 5. iGlu_AdaBoost: Identification of Lysine Glutarylation Using the AdaBoost Classifier.
    Dou L; Li X; Zhang L; Xiang H; Xu L
    J Proteome Res; 2021 Jan; 20(1):191-201. PubMed ID: 33090794
    [TBL] [Abstract][Full Text] [Related]  

  • 6. RF-GlutarySite: a random forest based predictor for glutarylation sites.
    Al-Barakati HJ; Saigo H; Newman RH; Kc DB
    Mol Omics; 2019 Jun; 15(3):189-204. PubMed ID: 31025681
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Mal-Lys: prediction of lysine malonylation sites in proteins integrated sequence-based features with mRMR feature selection.
    Xu Y; Ding YX; Ding J; Wu LY; Xue Y
    Sci Rep; 2016 Dec; 6():38318. PubMed ID: 27910954
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features.
    Arafat ME; Ahmad MW; Shovan SM; Dehzangi A; Dipta SR; Hasan MAM; Taherzadeh G; Shatabda S; Sharma A
    Genes (Basel); 2020 Aug; 11(9):. PubMed ID: 32878321
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Proteome-wide Lysine Glutarylation Profiling of the Mycobacterium tuberculosis H37Rv.
    Xie L; Wang G; Yu Z; Zhou M; Li Q; Huang H; Xie J
    J Proteome Res; 2016 Apr; 15(4):1379-85. PubMed ID: 26903315
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. 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]  

  • 12. iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions.
    Naseer S; Ali RF; Khan YD; Dominic PDD
    J Biomol Struct Dyn; 2022; 40(22):11691-11704. PubMed ID: 34396935
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Prediction of Protein Lysine Acylation by Integrating Primary Sequence Information with Multiple Functional Features.
    Du Y; Zhai Z; Li Y; Lu M; Cai T; Zhou B; Huang L; Wei T; Li T
    J Proteome Res; 2016 Dec; 15(12):4234-4244. PubMed ID: 27774790
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A systematic identification of species-specific protein succinylation sites using joint element features information.
    Hasan MM; Khatun MS; Mollah MNH; Yong C; Guo D
    Int J Nanomedicine; 2017; 12():6303-6315. PubMed ID: 28894368
    [TBL] [Abstract][Full Text] [Related]  

  • 15. MDCAN-Lys: A Model for Predicting Succinylation Sites Based on Multilane Dense Convolutional Attention Network.
    Wang H; Zhao H; Yan Z; Zhao J; Han J
    Biomolecules; 2021 Jun; 11(6):. PubMed ID: 34208298
    [TBL] [Abstract][Full Text] [Related]  

  • 16. GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier.
    Liu X; Zhu B; Dai XW; Xu ZA; Li R; Qian Y; Lu YP; Zhang W; Liu Y; Zheng J
    BMC Genomics; 2023 Dec; 24(1):765. PubMed ID: 38082413
    [TBL] [Abstract][Full Text] [Related]  

  • 17. FCCCSR_Glu: a semi-supervised learning model based on FCCCSR algorithm for prediction of glutarylation sites.
    Ning Q; Qi Z; Wang Y; Deng A; Chen C
    Brief Bioinform; 2022 Nov; 23(6):. PubMed ID: 36168700
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Preparation of Affinity Purified Antibodies against ε-Glutaryl-Lysine Residues in Proteins for Investigation of Glutarylated Proteins in Animal Tissues.
    Artiukhov AV; Kolesanova EF; Boyko AI; Chashnikova AA; Gnedoy SN; Kaehne T; Ivanova DA; Kolesnichenko AV; Aleshin VA; Bunik VI
    Biomolecules; 2021 Aug; 11(8):. PubMed ID: 34439834
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix.
    Chandra A; Sharma A; Dehzangi A; Shigemizu D; Tsunoda T
    BMC Mol Cell Biol; 2019 Dec; 20(Suppl 2):57. PubMed ID: 31856704
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 11.