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 *

282 related articles for article (PubMed ID: 28483566)

  • 1. PSSM-Suc: Accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction.
    Dehzangi A; López Y; Lal SP; Taherzadeh G; Michaelson J; Sattar A; Tsunoda T; Sharma A
    J Theor Biol; 2017 Jul; 425():97-102. PubMed ID: 28483566
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Success: evolutionary and structural properties of amino acids prove effective for succinylation site prediction.
    López Y; Sharma A; Dehzangi A; Lal SP; Taherzadeh G; Sattar A; Tsunoda T
    BMC Genomics; 2018 Jan; 19(Suppl 1):923. PubMed ID: 29363424
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. SucStruct: Prediction of succinylated lysine residues by using structural properties of amino acids.
    López Y; Dehzangi A; Lal SP; Taherzadeh G; Michaelson J; Sattar A; Tsunoda T; Sharma A
    Anal Biochem; 2017 Jun; 527():24-32. PubMed ID: 28363440
    [TBL] [Abstract][Full Text] [Related]  

  • 5. EvolStruct-Phogly: incorporating structural properties and evolutionary information from profile bigrams for the phosphoglycerylation prediction.
    Chandra AA; Sharma A; Dehzangi A; Tsunoda T
    BMC Genomics; 2019 Apr; 19(Suppl 9):984. PubMed ID: 30999859
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams.
    Dehzangi A; López Y; Lal SP; Taherzadeh G; Sattar A; Tsunoda T; Sharma A
    PLoS One; 2018; 13(2):e0191900. PubMed ID: 29432431
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A method to distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis.
    Zhou Y; Zhang N; Li BQ; Huang T; Cai YD; Kong XY
    J Biomol Struct Dyn; 2015; 33(11):2479-90. PubMed ID: 25616595
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Succinylation Site Prediction Based on Protein Sequences Using the IFS-LightGBM (BO) Model.
    Zhang L; Liu M; Qin X; Liu G
    Comput Math Methods Med; 2020; 2020():8858489. PubMed ID: 33224267
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. 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]  

  • 11. PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids.
    Chandra A; Sharma A; Dehzangi A; Ranganathan S; Jokhan A; Chou KC; Tsunoda T
    Sci Rep; 2018 Dec; 8(1):17923. PubMed ID: 30560923
    [TBL] [Abstract][Full Text] [Related]  

  • 12. PPIevo: protein-protein interaction prediction from PSSM based evolutionary information.
    Zahiri J; Yaghoubi O; Mohammad-Noori M; Ebrahimpour R; Masoudi-Nejad A
    Genomics; 2013 Oct; 102(4):237-42. PubMed ID: 23747746
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Characterization and Identification of Lysine Succinylation Sites based on Deep Learning Method.
    Huang KY; Hsu JB; Lee TY
    Sci Rep; 2019 Nov; 9(1):16175. PubMed ID: 31700141
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Investigation and identification of protein carbonylation sites based on position-specific amino acid composition and physicochemical features.
    Weng SL; Huang KY; Kaunang FJ; Huang CH; Kao HJ; Chang TH; Wang HY; Lu JJ; Lee TY
    BMC Bioinformatics; 2017 Mar; 18(Suppl 3):66. PubMed ID: 28361707
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. RAM-PGK: Prediction of Lysine Phosphoglycerylation Based on Residue Adjacency Matrix.
    Chandra AA; Sharma A; Dehzangi A; Tsunoda T
    Genes (Basel); 2020 Dec; 11(12):. PubMed ID: 33419274
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art.
    Walia RR; Caragea C; Lewis BA; Towfic F; Terribilini M; El-Manzalawy Y; Dobbs D; Honavar V
    BMC Bioinformatics; 2012 May; 13():89. PubMed ID: 22574904
    [TBL] [Abstract][Full Text] [Related]  

  • 19. SVM based prediction of RNA-binding proteins using binding residues and evolutionary information.
    Kumar M; Gromiha MM; Raghava GP
    J Mol Recognit; 2011; 24(2):303-13. PubMed ID: 20677174
    [TBL] [Abstract][Full Text] [Related]  

  • 20. PSSM-Sumo: deep learning based intelligent model for prediction of sumoylation sites using discriminative features.
    Khan S; AlQahtani SA; Noor S; Ahmad N
    BMC Bioinformatics; 2024 Aug; 25(1):284. PubMed ID: 39215231
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.