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 *

143 related articles for article (PubMed ID: 28252167)

  • 1. Prediction of phosphorylation sites based on the integration of multiple classifiers.
    Han RZ; Wang D; Chen YH; Dong LK; Fan YL
    Genet Mol Res; 2017 Feb; 16(1):. PubMed ID: 28252167
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition.
    Hayat M; Khan A
    J Theor Biol; 2011 Feb; 271(1):10-7. PubMed ID: 21110985
    [TBL] [Abstract][Full Text] [Related]  

  • 3. GANNPhos: a new phosphorylation site predictor based on a genetic algorithm integrated neural network.
    Tang YR; Chen YZ; Canchaya CA; Zhang Z
    Protein Eng Des Sel; 2007 Aug; 20(8):405-12. PubMed ID: 17652129
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition.
    Ahmad K; Waris M; Hayat M
    J Membr Biol; 2016 Jun; 249(3):293-304. PubMed ID: 26746980
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Prediction of microRNA-binding residues in protein using a Laplacian support vector machine based on sequence information.
    Ma X; Guo J; Sun X
    J Bioinform Comput Biol; 2018 Jun; 16(3):1840009. PubMed ID: 29591488
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Prediction of Protein Phosphorylation Sites by Integrating Secondary Structure Information and Other One-Dimensional Structural Properties.
    Dou Y; Yao B; Zhang C
    Methods Mol Biol; 2017; 1484():265-274. PubMed ID: 27787832
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Statistical geometry based prediction of nonsynonymous SNP functional effects using random forest and neuro-fuzzy classifiers.
    Barenboim M; Masso M; Vaisman II; Jamison DC
    Proteins; 2008 Jun; 71(4):1930-9. PubMed ID: 18186470
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Using core hydrophobicity to identify phosphorylation sites of human G protein-coupled receptors.
    Huang JH; Cao DS; Yan J; Xu QS; Hu QN; Liang YZ
    Biochimie; 2012 Aug; 94(8):1697-704. PubMed ID: 22503742
    [TBL] [Abstract][Full Text] [Related]  

  • 9. PhosphoSVM: prediction of phosphorylation sites by integrating various protein sequence attributes with a support vector machine.
    Dou Y; Yao B; Zhang C
    Amino Acids; 2014 Jun; 46(6):1459-69. PubMed ID: 24623121
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. MOWGLI: prediction of protein-MannOse interacting residues With ensemble classifiers usinG evoLutionary Information.
    Pai PP; Mondal S
    J Biomol Struct Dyn; 2016 Oct; 34(10):2069-83. PubMed ID: 26457920
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Boosting phosphorylation site prediction with sequence feature-based machine learning.
    Maiti S; Hassan A; Mitra P
    Proteins; 2020 Feb; 88(2):284-291. PubMed ID: 31412138
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Structural classification of proteins using texture descriptors extracted from the cellular automata image.
    Kavianpour H; Vasighi M
    Amino Acids; 2017 Feb; 49(2):261-271. PubMed ID: 27778167
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Support vector machine based classification of 3-dimensional protein physicochemical environments for automated function annotation.
    Min H; Yu S; Lee T; Yoon S
    Arch Pharm Res; 2010 Sep; 33(9):1451-9. PubMed ID: 20945145
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A novel method for prediction of protein interaction sites based on integrated RBF neural networks.
    Chen Y; Xu J; Yang B; Zhao Y; He W
    Comput Biol Med; 2012 Apr; 42(4):402-7. PubMed ID: 22226645
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Machine learning study of classifiers trained with biophysiochemical properties of amino acids to predict fibril forming Peptide motifs.
    Kumaran Nair SS; Subba Reddy NV; Hareesha KS
    Protein Pept Lett; 2012 Sep; 19(9):917-23. PubMed ID: 22486618
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Correlation and prediction of gene expression level from amino acid and dipeptide composition of its protein.
    Raghava GP; Han JH
    BMC Bioinformatics; 2005 Mar; 6():59. PubMed ID: 15773999
    [TBL] [Abstract][Full Text] [Related]  

  • 19. iSulf-Cys: Prediction of S-sulfenylation Sites in Proteins with Physicochemical Properties of Amino Acids.
    Xu Y; Ding J; Wu LY
    PLoS One; 2016; 11(4):e0154237. PubMed ID: 27104833
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A Treatise to Computational Approaches Towards Prediction of Membrane Protein and Its Subtypes.
    Butt AH; Rasool N; Khan YD
    J Membr Biol; 2017 Feb; 250(1):55-76. PubMed ID: 27866233
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.